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Chapter 4 - Neuroimaging of the Aging Brain

Published online by Cambridge University Press:  30 November 2019

Kenneth M. Heilman
Affiliation:
University of Florida
Stephen E. Nadeau
Affiliation:
University of Florida
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Summary

Neuroimaging visualizes and quantifies age-related changes in brain structure, function, cerebral blood flow, and cerebral metabolic health. MRI studies show reductions in both overall and regional brain volumes, but to a lesser extent than in Alzheimer’s disease. Those aging non-pathologically tend to have relative preservation of mesial temporal and enthorhinal brain areas. White matter changes are also common as shown by hyperintensities on fluid attenuated inversion recovery and other T2 MRI images, presumably as a result of co-morbities that increasingly occur with age. Diffusion tensor imaging shows reductions in white matter integrity, including white matter fiber counts and overall white matter volume, beginning in mid- to late life. The neural response during both rest and task performance also shows reduced activation of core task-related networks but expansion to include other region activation. Reduced cerebral blood volume and flow also occur, likely reflecting alterations in hemodynamic function due to cerebrovascular and cardiovascular changes. Cerebral metabolic changes on MR spectroscopy occur with reduced concentrations of GABA and other neurotransmitters, as well as markers of neuronal integrity. Myoinositol, a marker of glial activation, may be elevated, indicating neuroinflammation, though this effect is likely not ubiquitous in successful aging.

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Publisher: Cambridge University Press
Print publication year: 2019

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References

Huang, W., et al., High brain myo-inositol levels in the predementia phase of Alzheimer’s disease in adults with Down’s syndrome: a 1H MRS study. Am J Psychiatry, 1999. 156(12):1879–86.Google Scholar
Paul, E.J., et al., Dissociable brain biomarkers of fluid intelligence. Neuroimage, 2016. 137:201211.CrossRefGoogle ScholarPubMed
Wang, X.C., et al., Correlation between choline signal intensity and acetylcholine level in different brain regions of rat. Neurochem Res, 2008. 33(5):814–19.CrossRefGoogle ScholarPubMed
Cohen, R.A., et al., Cerebral metabolite abnormalities in human immunodeficiency virus are associated with cortical and subcortical volumes. J Neurovirol, 2010. 16(6):435–44.Google Scholar
Harezlak, J., et al., Persistence of HIV-associated cognitive impairment, inflammation, and neuronal injury in era of highly active antiretroviral treatment. AIDS, 2011. 25(5):625–33.Google Scholar
Harezlak, J., et al., Predictors of CNS injury as measured by proton magnetic resonance spectroscopy in the setting of chronic HIV infection and CART. J Neurovirol, 2014. 20(3):294303.Google Scholar
Hua, X., et al., Disrupted cerebral metabolite levels and lower nadir CD4+ counts are linked to brain volume deficits in 210 HIV-infected patients on stable treatment. Neuroimage Clin, 2013. 3:132–42.Google Scholar
Long, Z., et al., Vulnerability of welders to manganese exposure – a neuroimaging study. Neurotoxicology, 2014. 45:285–92.CrossRefGoogle ScholarPubMed
Zimmerman, M.E., et al., The relationship between frontal gray matter volume and cognition varies across the healthy adult lifespan. Am J Geriatr Psychiatry, 2006. 14(10):823–33.CrossRefGoogle ScholarPubMed
Bartzokis, G., et al., Age-related changes in frontal and temporal lobe volumes in men: a magnetic resonance imaging study. Arch Gen Psychiatry, 2001. 58(5):461–5.Google Scholar
Raz, N., et al., Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cereb Cortex, 1997. 7(3):268–82.Google Scholar
Raz, N., 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.Google Scholar
Resnick, S.M., et al., Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci, 2003. 23(8):3295–301.Google Scholar
Scahill, R.I., et al., A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch Neurol, 2003. 60(7):989–94.Google Scholar
Sullivan, E.V., et al., Effects of age and sex on volumes of the thalamus, pons, and cortex. Neurobiol Aging, 2004. 25(2):185–92.CrossRefGoogle ScholarPubMed
Jernigan, T.L., et al., Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol Aging, 2001. 22(4):581–94.CrossRefGoogle ScholarPubMed
Pfefferbaum, A., et al., A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Arch Neurol, 1994. 51(9):874–87.Google Scholar
Raz, N., et al., Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex, 2005. 15(11):1676–89.CrossRefGoogle ScholarPubMed
Ziegler, G., et al., Estimating anatomical trajectories with Bayesian mixed-effects modeling. Neuroimage, 2015. 121:5168.Google Scholar
Madsen, S.K., et al., Mapping ventricular expansion onto cortical gray matter in older adults. Neurobiol Aging, 2015. 36 (Suppl 1):S32S41.CrossRefGoogle ScholarPubMed
Metastasio, A., et al., Conversion of MCI to dementia: role of proton magnetic resonance spectroscopy. Neurobiol Aging, 2006. 27(7):926–32.CrossRefGoogle ScholarPubMed
Sabayan, B., et al., Cerebrovascular hemodynamics in Alzheimer’s disease and vascular dementia: a meta-analysis of transcranial Doppler studies. Ageing Res Rev, 2012. 11(2):271–7.CrossRefGoogle ScholarPubMed
Hughes, E.J., et al., Regional changes in thalamic shape and volume with increasing age. Neuroimage, 2012. 63(3):1134–42.Google Scholar
Yang, X., et al., Evolution of hippocampal shapes across the human lifespan. Hum Brain Mapp, 2013. 34(11):3075–85.CrossRefGoogle ScholarPubMed
Gerardin, E., et al., Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage, 2009. 47(4):1476–86.CrossRefGoogle ScholarPubMed
Miller, M.I., et al., Collaborative computational anatomy: an MRI morphometry study of the human brain via diffeomorphic metric mapping. Hum Brain Mapp, 2009. 30(7):2132–41.Google Scholar
Xu, Y., et al., Age effects on hippocampal structural changes in old men: the HAAS. Neuroimage, 2008. 40(3):1003–15.Google Scholar
Scher, A.I., et al., Hippocampal shape analysis in Alzheimer’s disease: a population-based study. Neuroimage, 2007. 36(1):818.Google Scholar
Wang, L., et al., Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type. Neuroimage, 2006. 30(1):5260.Google Scholar
Bouix, S., et al., Hippocampal shape analysis using medial surfaces. Neuroimage, 2005. 25(4):1077–89.Google Scholar
Wang, L., et al., Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging. Neuroimage, 2003. 20(2):667–82.CrossRefGoogle ScholarPubMed
Brickman, A.M., et al., Regional white matter and neuropsychological functioning across the adult lifespan. Biol Psychiatry, 2006. 60(5):444–53.CrossRefGoogle ScholarPubMed
Blatter, D.D., et al., Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life. AJNR Am J Neuroradiol, 1995. 16(2):241–51.Google Scholar
Jernigan, T.L., Press, G.A., and Hesselink, J.R., Methods for measuring brain morphologic features on magnetic resonance images. Validation and normal aging. Arch Neurol, 1990. 47(1):2732.Google Scholar
Sullivan, E.V., et al., Sex differences in corpus callosum size: relationship to age and intracranial size. Neurobiol Aging, 2001. 22(4):603–11.Google Scholar
Pfefferbaum, A., et al., A controlled study of cortical gray matter and ventricular changes in alcoholic men over a 5-year interval. Arch Gen Psychiatry, 1998. 55(10):905–12.Google Scholar
Aasprang, A., et al., Ten-year changes in health-related quality of life after biliopancreatic diversion with duodenal switch. Surg Obes Relat Dis, 2016. 12(8):1594–600.Google Scholar
Ge, Y., et al., Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. AJNR Am J Neuroradiol, 2002. 23(8):1327–33.Google Scholar
Driscoll, I., et al., Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology, 2009. 72(22):1906–13.Google Scholar
Pfefferbaum, A., et al., Age-related decline in brain white matter anisotropy measured with spatially corrected echo-planar diffusion tensor imaging. Magn Reson Med, 2000. 44(2):259–68.3.0.CO;2-6>CrossRefGoogle ScholarPubMed
de Groot, M., et al., Tract-specific white matter degeneration in aging: the Rotterdam Study. Alzheimers Dement, 2015. 11(3):321–30.Google Scholar
Davis, S.W., et al., Assessing the effects of age on long white matter tracts using diffusion tensor tractography. Neuroimage, 2009. 46(2):530–41.CrossRefGoogle Scholar
Aboitiz, F., et al., Age-related changes in fibre composition of the human corpus callosum: sex differences. Neuroreport, 1996. 7(11):1761–4.CrossRefGoogle ScholarPubMed
Bartzokis, G., Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging, 2004. 25(1):518; author reply 49–62.Google Scholar
Sexton, C.E., et al., Accelerated changes in white matter microstructure during aging: a longitudinal diffusion tensor imaging study. J Neurosci, 2014. 34(46):15425–36.Google Scholar
Bennett, I.J., et al., Age-related differences in multiple measures of white matter integrity: a diffusion tensor imaging study of healthy aging. Hum Brain Mapp, 2010. 31(3):378–90.Google Scholar
Westlye, L.T., et al., Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. Cereb Cortex, 2010. 20(9):2055–68.Google Scholar
Zhang, H., et al., NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012. 61(4):1000–16.Google Scholar
Jacobs, B., Driscoll, L., and 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(4):661–80.3.0.CO;2-N>CrossRefGoogle ScholarPubMed
Beaulieu, C., The biological basis of diffusion anisotropy, in Diffusion MRI: From Quantitative Measurement to In-vivo Neuroanatomy, Johansen-Berg, H. and Behrens, T.E.J., editors. London: Academic Press, 2009, pp. 155–84.Google Scholar
Pierpaoli, C., et al., Diffusion tensor MR imaging of the human brain. Radiology, 1996. 201(3):637–48.Google Scholar
Nazeri, A., et al., Functional consequences of neurite orientation dispersion and density in humans across the adult lifespan. J Neurosci, 2015. 35(4):1753–62.Google Scholar
Flood, D.G., et al., Age-related dendritic growth in dentate gyrus of human brain is followed by regression in the “oldest old.” Brain Res, 1985. 345(2):366–8.Google Scholar
Pyapali, G.K. and Turner, D.A., Increased dendritic extent in hippocampal CA1 neurons from aged F344 rats. Neurobiol Aging, 1996. 17(4):601–11.Google Scholar
Colgan, N., et al., Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer’s disease. Neuroimage, 2016. 125:739–44.Google Scholar
Sala-Llonch, R., Bartres-Faz, D., and Junque, C., Reorganization of brain networks in aging: a review of functional connectivity studies. Front Psychol, 2015. 6:663.Google Scholar
Anthony, M. and Lin, F., A systematic review for functional neuroimaging studies of cognitive reserve across the cognitive aging spectrum. Arch Clin Neuropsychol, 2018. 33(8):937–48.Google Scholar
Dennis, E.L. and Thompson, P.M., Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol Rev, 2014. 24(1):4962.Google Scholar
Ouchi, Y. and Kikuchi, M., A review of the default mode network in aging and dementia based on molecular imaging. Rev Neurosci, 2012. 23(3):263–8.Google Scholar
Wang, L., et al., Amnestic mild cognitive impairment: topological reorganization of the default-mode network. Radiology, 2013. 268(2):501–14.CrossRefGoogle ScholarPubMed
Lee, A., Tan, M., and Qiu, A., Distinct aging effects on functional networks in good and poor cognitive performers. Front Aging Neurosci, 2016. 8:215.Google Scholar
Onoda, K., Ishihara, M., and Yamaguchi, S., Decreased functional connectivity by aging is associated with cognitive decline. J Cogn Neurosci, 2012. 24(11):2186–98.Google Scholar
Joo, S.H., Lim, H.K., and Lee, C.U., Three large-scale functional brain networks from resting-state functional MRI in subjects with different levels of cognitive impairment. Psychiatry Investig, 2016. 13(1):17.CrossRefGoogle ScholarPubMed
Cieri, F. and Esposito, R., Neuroaging through the lens of the resting state networks. Biomed Res Int, 2018. 2018:5080981.Google Scholar
Chhatwal, J.P. and Sperling, R.A., Functional MRI of mnemonic networks across the spectrum of normal aging, mild cognitive impairment, and Alzheimer’s disease. J Alzheimers Dis, 2012. 31 (Suppl 3):S155S167.CrossRefGoogle ScholarPubMed
Cabeza, R., et al., Task-independent and task-specific age effects on brain activity during working memory, visual attention and episodic retrieval. Cereb Cortex, 2004. 14(4):364–75.Google Scholar
Paskavitz, J.F., et al., Recruitment and stabilization of brain activation within a working memory task; an FMRI study. Brain Imaging Behav, 2010. 4(1):521.Google Scholar
Cabeza, R., et al., Aging gracefully: compensatory brain activity in high-performing older adults. Neuroimage, 2002. 17(3):1394–402.Google Scholar
Spreng, R.N., et al., Attenuated anticorrelation between the default and dorsal attention networks with aging: evidence from task and rest. Neurobiol Aging, 2016. 45:149–60.Google Scholar
Schmitz, T.W., et al., Distinguishing attentional gain and tuning in young and older adults. Neurobiol Aging, 2014. 35(11):2514–25.Google Scholar
Russell, C., et al., Dynamic attentional modulation of vision across space and time after right hemisphere stroke and in ageing. Cortex, 2013. 49(7):1874–83.CrossRefGoogle ScholarPubMed
Oren, N., et al., How attention modulates encoding of dynamic stimuli in older adults. Behav Brain Res, 2018. 347:209–18.Google Scholar
Mitchell, K.J., et al., Age differences in brain activity during perceptual versus reflective attention. Neuroreport, 2010. 21(4):293–7.Google Scholar
Madden, D.J., et al., Adult age differences in the functional neuroanatomy of visual attention: a combined fMRI and DTI study. Neurobiol Aging, 2007. 28(3):459–76.Google Scholar
Kurth, S., et al., Effects of aging on task- and stimulus-related cerebral attention networks. Neurobiol Aging, 2016. 44:8595.CrossRefGoogle ScholarPubMed
Kim, S.Y. and Giovanello, K.S., The effects of attention on age-related relational memory deficits: fMRI evidence from a novel attentional manipulation. J Cogn Neurosci, 2011. 23(11):3637–56.Google Scholar
Hampshire, A., et al., Inefficiency in self-organized attentional switching in the normal aging population is associated with decreased activity in the ventrolateral prefrontal cortex. J Cogn Neurosci, 2008. 20(9):1670–86.Google Scholar
Geerligs, L., et al., Brain mechanisms underlying the effects of aging on different aspects of selective attention. Neuroimage, 2014. 91:5262.Google Scholar
Zanto, T.P. and Gazzaley, A., Fronto-parietal network: flexible hub of cognitive control. Trends Cogn Sci, 2013. 17(12):602–3.Google Scholar
Van Impe, A., et al., Age-related changes in brain activation underlying single- and dual-task performance: visuomanual drawing and mental arithmetic. Neuropsychologia, 2011. 49(9):2400–9.Google Scholar
Hartley, A.A., Jonides, J., and Sylvester, C.Y., Dual-task processing in younger and older adults: similarities and differences revealed by fMRI. Brain Cogn, 2011. 75(3):281–91.Google Scholar
Nagamatsu, L.S., et al., The neurocognitive basis for impaired dual-task performance in senior fallers. Front Aging Neurosci, 2016. 8:20.Google Scholar
Gauthier, C.J., et al., Absolute quantification of resting oxygen metabolism and metabolic reactivity during functional activation using QUO2 MRI. Neuroimage, 2012. 63(3):1353–63.Google Scholar
Goodwin, J.A., et al., Quantitative fMRI using hyperoxia calibration: reproducibility during a cognitive Stroop task. Neuroimage, 2009. 47(2):573–80.CrossRefGoogle ScholarPubMed
Mildner, T., et al., Towards quantification of blood-flow changes during cognitive task activation using perfusion-based fMRI. Neuroimage, 2005. 27(4):919–26.Google Scholar
Mohtasib, R.S., et al., Calibrated fMRI during a cognitive Stroop task reveals reduced metabolic response with increasing age. Neuroimage, 2012. 59(2):1143–51.Google Scholar
Tam, A., et al., Effects of reaction time variability and age on brain activity during Stroop task performance. Brain Imaging Behav, 2015. 9(3):609–18.Google Scholar
Fjell, A.M., et al., The disconnected brain and executive function decline in aging. Cereb Cortex, 2017. 27(3):2303–17.Google Scholar
Eich, T.S., et al., Functional brain and age-related changes associated with congruency in task switching. Neuropsychologia, 2016. 91:211–21.Google Scholar
Takeuchi, H., et al., Failing to deactivate: the association between brain activity during a working memory task and creativity. Neuroimage, 2011. 55(2):681–7.CrossRefGoogle ScholarPubMed
Oren, N., et al., Neural patterns underlying the effect of negative distractors on working memory in older adults. Neurobiol Aging, 2017. 53:93102.Google Scholar
Nyberg, L., et al., Neural correlates of variable working memory load across adult age and skill: dissociative patterns within the fronto-parietal network. Scand J Psychol, 2009. 50(1):41–6.Google Scholar
Nagel, I.E., et al., Load modulation of BOLD response and connectivity predicts working memory performance in younger and older adults. J Cogn Neurosci, 2011. 23(8):2030–45.Google Scholar
Luis, E.O., et al., Successful working memory processes and cerebellum in an elderly sample: a neuropsychological and fMRI study. PLoS One, 2015. 10(7):e0131536.Google Scholar
Heinzel, S., et al., Prefrontal-parietal effective connectivity during working memory in older adults. Neurobiol Aging, 2017. 57:1827.Google Scholar
Hakun, J.G. and Johnson, N.F., Dynamic range of frontoparietal functional modulation is associated with working memory capacity limitations in older adults. Brain Cogn, 2017. 118:128–36.Google Scholar
Emery, L., et al., Age-related changes in neural activity during performance matched working memory manipulation. Neuroimage, 2008. 42(4):1577–86.Google Scholar
Grady, C.L., Yu, H., and Alain, C., Age-related differences in brain activity underlying working memory for spatial and nonspatial auditory information. Cereb Cortex, 2008. 18(1):189–99.Google Scholar
Shing, Y.L., et al., Neural activation patterns of successful episodic encoding: reorganization during childhood, maintenance in old age. Dev Cogn Neurosci, 2016. 20:5969.Google Scholar
Cooper, C.M., et al., Memory and functional brain differences in a national sample of U.S. veterans with Gulf War Illness. Psychiatry Res Neuroimaging, 2016. 250:3341.CrossRefGoogle Scholar
Wang, T.H., et al., The effects of age on the neural correlates of recollection success, recollection-related cortical reinstatement, and post-retrieval monitoring. Cereb Cortex, 2016. 26(4):1698–714.Google Scholar
Cansino, S., et al., fMRI subsequent source memory effects in young, middle-aged and old adults. Behav Brain Res, 2015. 280:2435.Google Scholar
Maillet, D. and Rajah, M.N., Age-related differences in brain activity in the subsequent memory paradigm: a meta-analysis. Neurosci Biobehav Rev, 2014. 45:246–57.Google Scholar
Mattson, J.T., et al., Effects of age on negative subsequent memory effects associated with the encoding of item and item-context information. Cereb Cortex, 2014. 24(12):3322–33.Google Scholar
Angel, L., et al., Differential effects of aging on the neural correlates of recollection and familiarity. Cortex, 2013. 49(6):1585–97.Google Scholar
Sambataro, F., et al., Normal aging modulates prefrontoparietal networks underlying multiple memory processes. Eur J Neurosci, 2012. 36(11):3559–67.Google Scholar
Salami, A., Eriksson, J., and Nyberg, L., Opposing effects of aging on large-scale brain systems for memory encoding and cognitive control. J Neurosci, 2012. 32(31):10749–57.Google Scholar
Ramsoy, T.Z., et al., Healthy aging attenuates task-related specialization in the human medial temporal lobe. Neurobiol Aging, 2012. 33(9):1874–89.Google Scholar
Maillet, D. and Rajah, M.N., Age-related changes in the three-way correlation between anterior hippocampus volume, whole-brain patterns of encoding activity and subsequent context retrieval. Brain Res, 2011. 1420:6879.Google Scholar
Protzner, A.B., et al., Network interactions explain effective encoding in the context of medial temporal damage in MCI. Hum Brain Mapp, 2011. 32(8):1277–89.Google Scholar
Trivedi, M.A., et al., fMRI activation changes during successful episodic memory encoding and recognition in amnestic mild cognitive impairment relative to cognitively healthy older adults. Dement Geriatr Cogn Disord, 2008. 26(2):123–37.Google Scholar
Kircher, T., et al., Anterior hippocampus orchestrates successful encoding and retrieval of non-relational memory: an event-related fMRI study. Eur Arch Psychiatry Clin Neurosci, 2008. 258(6):363–72.Google Scholar
Bangen, K.J., et al., Differential age effects on cerebral blood flow and BOLD response to encoding: associations with cognition and stroke risk. Neurobiol Aging, 2009. 30(8):1276–87.Google Scholar
Han, S.D., et al., Verbal paired-associate learning by APOE genotype in non-demented older adults: fMRI evidence of a right hemispheric compensatory response. Neurobiol Aging, 2007. 28(2):238–47.Google Scholar
Rand-Giovannetti, E., et al., Hippocampal and neocortical activation during repetitive encoding in older persons. Neurobiol Aging, 2006. 27(1):173–82.Google Scholar
Vandenbroucke, M.W., et al., Interindividual differences of medial temporal lobe activation during encoding in an elderly population studied by fMRI. Neuroimage, 2004. 21(1):173–80.Google Scholar
Morcom, A.M., et al., Age effects on the neural correlates of successful memory encoding. Brain, 2003. 126(Pt. 1):213–29.Google Scholar
Cabeza, R., Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol Aging, 2002. 17(1):85100.CrossRefGoogle ScholarPubMed
Park, D.C., et al., Working memory for complex scenes: age differences in frontal and hippocampal activations. J Cogn Neurosci, 2003. 15(8):1122–34.Google Scholar
Otsuka, Y., et al., Decreased activation of anterior cingulate cortex in the working memory of the elderly. Neuroreport, 2006. 17(14):1479–82.CrossRefGoogle ScholarPubMed
Corkin, S., Functional MRI for studying episodic memory in aging and Alzheimer’s disease. Geriatrics, 1998. 53 (Suppl 1):S13S15.Google Scholar
Clement, F. and Belleville, S., Test-retest reliability of fMRI verbal episodic memory paradigms in healthy older adults and in persons with mild cognitive impairment. Hum Brain Mapp, 2009. 30(12):4033–47.Google Scholar
Dennis, N.A., Kim, H., and Cabeza, R., Age-related differences in brain activity during true and false memory retrieval. J Cogn Neurosci, 2008. 20(8):1390–402.Google Scholar
Rajah, M.N. and McIntosh, A.R., Age-related differences in brain activity during verbal recency memory. Brain Res, 2008. 1199:111–25.Google Scholar
Stevens, W.D., et al., A neural mechanism underlying memory failure in older adults. J Neurosci, 2008. 28(48):12820–4.Google Scholar
Bai, F., et al., Abnormal functional connectivity of hippocampus during episodic memory retrieval processing network in amnestic mild cognitive impairment. Biol Psychiatry, 2009. 65(11):951–8.Google Scholar
Spaniol, J. and Grady, C., Aging and the neural correlates of source memory: over-recruitment and functional reorganization. Neurobiol Aging, 2012. 33(2):425 e3–18.Google Scholar
St Jacques, P.L., Rubin, D.C., and Cabeza, R., Age-related effects on the neural correlates of autobiographical memory retrieval. Neurobiol Aging, 2012. 33(7):1298–310.Google Scholar
Sugarman, M.A., et al., Functional magnetic resonance imaging of semantic memory as a presymptomatic biomarker of Alzheimer’s disease risk. Biochim Biophys Acta, 2012. 1822(3):442–56.Google Scholar
Geddes, M.R., et al., Human aging reduces the neurobehavioral influence of motivation on episodic memory. Neuroimage, 2018. 171:296310.Google Scholar
Huijbers, W., et al., Explaining the encoding/retrieval flip: memory-related deactivations and activations in the posteromedial cortex. Neuropsychologia, 2012. 50(14):3764–74.Google Scholar
MacDonald, S.W., et al., Increased response-time variability is associated with reduced inferior parietal activation during episodic recognition in aging. J Cogn Neurosci, 2008. 20(5):779–86.Google Scholar
Foster, C.M., et al., Prefrontal contributions to relational encoding in amnestic mild cognitive impairment. Neuroimage Clin, 2016. 11:158–66.Google Scholar
Pudas, S., et al., Longitudinal evidence for increased functional response in frontal cortex for older adults with hippocampal atrophy and memory decline. Cereb Cortex, 2018. 28(3):936–48.Google Scholar
Garcia, A., Functional activation and connectivity of the semantic network in older adults. PhD diss., University of Florida, Gainesville, 2017.Google Scholar
Seider, T., Seeing brain: an FMRI study of age-related changes in visual perception and discrimination. PhD diss., University of Florida, Gainesville, 2017.Google Scholar
Alosco, M.L., et al., The synergistic effects of anxiety and cerebral hypoperfusion on cognitive dysfunction in older adults with cardiovascular disease. J Geriatr Psychiatry Neurol, 2015. 28(1):5766.Google Scholar
Alosco, M.L., et al., The adverse effects of reduced cerebral perfusion on cognition and brain structure in older adults with cardiovascular disease. Brain Behav, 2013. 3(6):626–36.CrossRefGoogle ScholarPubMed
Alosco, M.L., et al., The impact of hypertension on cerebral perfusion and cortical thickness in older adults. J Am Soc Hypertens, 2014. 8(8):561–70.Google Scholar
Brickman, A.M., et al., Reduction in cerebral blood flow in areas appearing as white matter hyperintensities on magnetic resonance imaging. Psychiatry Res, 2009. 172(2):117–20.Google Scholar
Zimmerman, B., et al., Cardiorespiratory fitness mediates the effects of aging on cerebral blood flow. Front Aging Neurosci, 2014. 6:59.Google Scholar
Leoni, R.F., et al., Cerebral blood flow and vasoreactivity in aging: an arterial spin labeling study. Braz J Med Biol Res, 2017. 50(4):e5670.Google Scholar
Du, A.T., et al., Hypoperfusion in frontotemporal dementia and Alzheimer disease by arterial spin labeling MRI. Neurology, 2006. 67(7):1215–20.Google Scholar
Fleisher, A.S., et al., Cerebral perfusion and oxygenation differences in Alzheimer’s disease risk. Neurobiol Aging, 2009. 30(11):1737–48.Google Scholar
Alexopoulos, P., et al., Perfusion abnormalities in mild cognitive impairment and mild dementia in Alzheimer’s disease measured by pulsed arterial spin labeling MRI. Eur Arch Psychiatry Clin Neurosci, 2012. 262(1):6977.Google Scholar
Gauthier, C.J., et al., Age dependence of hemodynamic response characteristics in human functional magnetic resonance imaging. Neurobiol Aging, 2013. 34(5):1469–85.Google Scholar
De Vis, J.B., et al., Arterial-spin-labeling (ASL) perfusion MRI predicts cognitive function in elderly individuals: a 4-year longitudinal study. J Magn Reson Imaging, 2018. 48(2):449–58.Google Scholar
Hays, C.C., et al., Subjective cognitive decline modifies the relationship between cerebral blood flow and memory function in cognitively normal older adults. J Int Neuropsychol Soc, 2018. 24(3):213–23.Google Scholar
Lee, C., et al., Imaging cerebral blood flow in the cognitively normal aging brain with arterial spin labeling: implications for imaging of neurodegenerative disease. J Neuroimaging, 2009. 19(4):344–52.Google Scholar
Tancredi, F.B., Lajoie, I., and Hoge, R.D., Test-retest reliability of cerebral blood flow and blood oxygenation level-dependent responses to hypercapnia and hyperoxia using dual-echo pseudo-continuous arterial spin labeling and step changes in the fractional composition of inspired gases. J Magn Reson Imaging, 2015. 42(4):1144–57.Google Scholar
Huppert, T.J., et al., Quantitative spatial comparison of diffuse optical imaging with blood oxygen level-dependent and arterial spin labeling-based functional magnetic resonance imaging. J Biomed Opt, 2006. 11(6):064018.Google Scholar
Huppert, T.J., et al., A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. Neuroimage, 2006. 29(2):368–82.CrossRefGoogle ScholarPubMed
De Vis, J.B., et al., Age-related changes in brain hemodynamics; a calibrated MRI study. Hum Brain Mapp, 2015. 36(10):3973–87.Google Scholar
Hoge, R.D., et al., Simultaneous recording of task-induced changes in blood oxygenation, volume, and flow using diffuse optical imaging and arterial spin-labeling MRI. Neuroimage, 2005. 25(3):701–7.Google Scholar
Bowtell, J.L., et al., Enhanced task-related brain activation and resting perfusion in healthy older adults after chronic blueberry supplementation. Appl Physiol Nutr Metab, 2017. 42(7):773–79.Google Scholar
Petersen, E.T., Lim, T., and Golay, X., Model-free arterial spin labeling quantification approach for perfusion MRI. Magn Reson Med, 2006. 55(2):219–32.CrossRefGoogle ScholarPubMed
Cebral, J.R., et al., Flow-area relationship in internal carotid and vertebral arteries. Physiol Meas, 2008. 29(5):585–94.Google Scholar
Tain, R.W., Ertl-Wagner, B., and Alperin, N., Influence of the compliance of the neck arteries and veins on the measurement of intracranial volume change by phase-contrast MRI. J Magn Reson Imaging, 2009. 30(4):878–83.Google Scholar
Teng, P.Y., Bagci, A.M., and Alperin, N., Automated prescription of an optimal imaging plane for measurement of cerebral blood flow by phase contrast magnetic resonance imaging. IEEE Trans Biomed Eng, 2011. 58(9):2566–73.Google Scholar
Haga, K.K., et al., A systematic review of brain metabolite changes, measured with 1H magnetic resonance spectroscopy, in healthy aging. Neurobiol Aging, 2009. 30(3):353–63.Google Scholar
Gao, F., et al., Edited magnetic resonance spectroscopy detects an age-related decline in brain GABA levels. Neuroimage, 2013. 78:7582.Google Scholar
Porges, E.C., et al., Impact of tissue correction strategy on GABA-edited MRS findings. Neuroimage, 2017. 162:249–56.Google Scholar
Porges, E.C., et al., Frontal gamma-aminobutyric acid concentrations are associated with cognitive performance in older adults. Biol Psychiatry Cogn Neurosci Neuroimaging, 2017. 2(1):3844.Google Scholar
Rae, C.D., A guide to the metabolic pathways and function of metabolites observed in human brain 1H magnetic resonance spectra. Neurochem Res, 2014. 39(1):136.Google Scholar
Ding, X.Q., et al., Physiological neuronal decline in healthy aging human brain – an in vivo study with MRI and short echo-time whole-brain (1)H MR spectroscopic imaging. Neuroimage, 2016. 137:4551.Google Scholar
Schmitz, B., et al., Effects of aging on the human brain: a proton and phosphorus MR spectroscopy study at 3T. J Neuroimaging, 2018. 28(4):416–21.Google Scholar
Harper, D.G., et al., Brain levels of high-energy phosphate metabolites and executive function in geriatric depression. Int J Geriatr Psychiatry, 2016. 31(11):1241–9.Google Scholar
Forester, B.P., et al., Age-related changes in brain energetics and phospholipid metabolism. NMR Biomed, 2010. 23(3):242–50.Google Scholar
Duarte, J.M., Girault, F.M., and Gruetter, R., Brain energy metabolism measured by (13)C magnetic resonance spectroscopy in vivo upon infusion of [3-(13)C]lactate. J Neurosci Res, 2015. 93(7):1009–18.Google Scholar
Maher, E.A., et al., Metabolism of [U-13 C]glucose in human brain tumors in vivo. NMR Biomed, 2012. 25(11):1234–44.Google Scholar
Lei, H., et al., Direct validation of in vivo localized 13C MRS measurements of brain glycogen. Magn Reson Med, 2007. 57(2):243–8.Google Scholar
Taylor, A., et al., Approaches to studies on neuronal/glial relationships by 13C-MRS analysis. Dev Neurosci, 1996. 18(5-6):434–42.Google Scholar
Isaacson, S.H., et al., Clinical utility of DaTscan imaging in the evaluation of patients with parkinsonism: a US perspective. Expert Rev Neurother, 2017. 17(3):219–25.Google Scholar
Kaasinen, V. and Vahlberg, T., Striatal dopamine in Parkinson disease: a meta-analysis of imaging studies. Ann Neurol, 2017. 82(6):873–82.CrossRefGoogle ScholarPubMed
Zou, J., et al., Position emission tomography/single-photon emission tomography neuroimaging for detection of premotor Parkinson’s disease. CNS Neurosci Ther, 2016. 22(3):167–77.Google Scholar
Wong, C.W., Quaranta, V., and Glenner, G.G., Neuritic plaques and cerebrovascular amyloid in Alzheimer disease are antigenically related. Proc Natl Acad Sci U S A, 1985. 82(24):8729–32.Google Scholar
Jack, C.R. Jr., et al., Introduction to the recommendations from the National Institute on Aging–Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement, 2011. 7(3):257–62.Google Scholar
Goedert, M., et al., Multiple isoforms of human microtubule-associated protein tau: sequences and localization in neurofibrillary tangles of Alzheimer’s disease. Neuron, 1989. 3(4):519–26.Google Scholar
McKee, A.C., et al., The spectrum of disease in chronic traumatic encephalopathy. Brain, 2013. 136(Pt. 1):4364.Google Scholar
Alafuzoff, I., Minimal neuropathologic diagnosis for brain banking in the normal middle-aged and aged brain and in neurodegenerative disorders, in Handbook of Clinical Neurology, B. Aminoff MJ, F, Swaab, DF, editors. New York: Elsevier, 2018, pp. 131–41.Google Scholar

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