Structural and Functional Imaging

  • Vimal M. Aga


The use of imaging studies in the clinical practice of psychiatry continues to lag behind the other clinical neurosciences. This explains the clinical psychiatrist’s relative unease with ordering and interpreting imaging studies. Even though various clinical practice guidelines in psychiatry recommend imaging studies as part of the diagnostic work-up for the psychiatric disorders, no signature imaging findings have been reported as diagnostic of any psychiatric disorder. This includes psychiatric disorders with onset later in life with the exception of the neurocognitive disorders. Over a decade of research in the role of diagnostic and prognostic biomarkers including imaging biomarkers in the neurocognitive disorders led to the framework first proposed by the International Working Group (IWG) in 2007 for diagnosing Alzheimer’s disease (AD) in which diagnostic biomarkers had a central role. This was subsequently expanded upon by the National Institute on Aging-Alzheimer’s Association (NIA-AA) criteria published in 2011. Around the same time, molecular imaging was approved for use in diagnosing some movement disorders. Consequently, imaging (and fluid) biomarkers have gradually found their way into memory and movement disorders clinics, and biomarkers now play an important role in categorizing several neurocognitive disorders as “possible” or “probable” as per the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-5) classification. This chapter provides a broad overview of the imaging modalities commonly used in the clinical practice of neuropsychiatry today that have documented diagnostic utility at the individual level. Indications for the use of imaging in the clinic are discussed first and a case is made for improving the training of psychiatrists in ordering and interpreting imaging studies. Next, structural, functional, and molecular imaging modalities are discussed in some detail, including computerized tomography (CT), magnetic resonance imaging (MRI), single photon emission computerized tomography (SPECT), positron emission tomography (PET), and dopamine transporter SPECT. Imaging modalities that are primarily used in research have not been covered unless the imaging modality is either a major breakthrough (functional MRI) or when it is expected that clinical use for that modality will be approved in the near future (amyloid PET, 123I-metaiodobenzylguanidine, MIBG myocardial scintigraphy). Practical aspects of archiving and viewing imaging studies are discussed at the end. Given that the clinical indications for the use of imaging studies in neuropsychiatry are still mostly restricted to the neurocognitive and movement disorders, the bulk of the chapter deals with the role of imaging studies in diagnosing these disorders. The role of imaging as a prognostic biomarker is outside the scope of this chapter. Also, the discussion in each section has been mostly limited to the underlying theory and general methodology, while the signature imaging findings in the individual disorders will be covered in the chapters on those disorders.


Structural imaging Functional imaging Biomarkers Computer tomography Magnetic resonance imaging Single photon emission computerized tomography Positron emission tomography 



The author gratefully acknowledges the contribution of Dr. David Pettersson, Assistant Professor in Neuroradiology, and Dr. Lisa Silbert, Associate Professor in Neurology and Director of the Neuroimaging Lab at the Layton Aging and Alzheimer’s Disease Center, Oregon Health & Science University, Portland, Oregon, who reviewed the final draft of this chapter and provided valuable feedback.


  1. 1.
    Jack CR Jr, Holtzman DM. Biomarker modeling of Alzheimer’s disease. Neuron. 2013;80(6):1347–58.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Davis J, Maes M, Andreazza A, McGrath JJ, Tye SJ, Berk M. Towards a classification of biomarkers of neuropsychiatric disease: from encompass to compass. Mol Psychiatry. 2015;20(2):152–3.CrossRefPubMedGoogle Scholar
  3. 3.
    Hoptman MJ, Gunning-Dixon FM, Murphy CF, Lim KO, Alexopoulos GS. Structural neuroimaging research methods in geriatric depression. Am J Geriatr Psychiatry. 2006;14(10):812–22.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    American Psychiatric A, American Psychiatric Association DSMTF. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, D.C.: American Psychiatric Association; 2013.CrossRefGoogle Scholar
  5. 5.
    Honea R, Crow TJ, Passingham D, Mackay CE. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry. 2005;162(12):2233–45.CrossRefPubMedGoogle Scholar
  6. 6.
    Arnone D, Cavanagh J, Gerber D, Lawrie SM, Ebmeier KP, McIntosh AM. Magnetic resonance imaging studies in bipolar disorder and schizophrenia: meta-analysis. Br J Psychiatry J Ment Sci. 2009;195(3):194–201.CrossRefGoogle Scholar
  7. 7.
    Buchsbaum MS, Ingvar DH, Kessler R, et al. Cerebral glucography with positron tomography. Use in normal subjects and in patients with schizophrenia. Arch Gen Psychiatry. 1982;39(3):251–9.CrossRefPubMedGoogle Scholar
  8. 8.
    Kupferschmidt DA, Zakzanis KK. Toward a functional neuroanatomical signature of bipolar disorder: quantitative evidence from the neuroimaging literature. Psychiatry Res. 2011;193(2):71–9.CrossRefPubMedGoogle Scholar
  9. 9.
    Hahn C, Lim HK, Lee CU. Neuroimaging findings in late-onset schizophrenia and bipolar disorder. J Geriatr Psychiatry Neurol. 2014;27(1):56–62.CrossRefPubMedGoogle Scholar
  10. 10.
    Weinberger DR, Radulescu E. Finding the elusive psychiatric "lesion" with 21st-century Neuroanatomy: a note of caution. Am J Psychiatry. 2016;173(1):27–33.CrossRefPubMedGoogle Scholar
  11. 11.
    Borsje P, Wetzels RB, Lucassen PL, Pot AM, Koopmans RT. The course of neuropsychiatric symptoms in community-dwelling patients with dementia: a systematic review. Int Psychogeriatr. 2015;27(3):385–405.CrossRefPubMedGoogle Scholar
  12. 12.
    Taragano F, Allegri R. Mild behavioral impairment: the early diagnosis. Paper presented at: International Psychogeriatrics. 2003.Google Scholar
  13. 13.
    Woolley JD, Khan BK, Murthy NK, Miller BL, Rankin KP. The diagnostic challenge of psychiatric symptoms in neurodegenerative disease: rates of and risk factors for prior psychiatric diagnosis in patients with early neurodegenerative disease. J Clin Psychiatry. 2011;72(2):126–33.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Lanata SC, Miller BL. The behavioural variant frontotemporal dementia (bvFTD) syndrome in psychiatry. J Neurol Neurosurg Psychiatry. 2016;87(5):501–11.CrossRefPubMedGoogle Scholar
  15. 15.
    Rosenberg PB, Mielke MM, Appleby BS, Oh ES, Geda YE, Lyketsos CG. The association of neuropsychiatric symptoms in MCI with incident dementia and Alzheimer disease. Am J Geriatr Psychiatry. 2013;21(7):685–95.CrossRefPubMedGoogle Scholar
  16. 16.
    Ducharme S, Price BH, Larvie M, Dougherty DD, Dickerson BC. Clinical approach to the differential diagnosis between behavioral variant Frontotemporal dementia and primary psychiatric disorders. Am J Psychiatry. 2015;172(9):827–37.CrossRefPubMedGoogle Scholar
  17. 17.
    Ismail Z, Smith EE, Geda Y, et al. Neuropsychiatric symptoms as early manifestations of emergent dementia: provisional diagnostic criteria for mild behavioral impairment. Alzheimers Dement. 2016;12(2):195–202.CrossRefPubMedGoogle Scholar
  18. 18.
    Ismail Z, Aguera-Ortiz L, Brodaty H, et al. The mild behavioral impairment checklist (MBI-C): a rating scale for neuropsychiatric symptoms in pre-dementia populations. J Alzheimers Dis. 2017;56(3):929–38.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Winklbaur B, Ebner N, Sachs G, Thau K, Fischer G. Substance abuse in patients with schizophrenia. Dialogues Clin Neurosci. 2006;8(1):37–43.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Fan Z, Wu Y, Shen J, Ji T, Zhan R. Schizophrenia and the risk of cardiovascular diseases: a meta-analysis of thirteen cohort studies. J Psychiatr Res. 2013;47(11):1549–56.CrossRefPubMedGoogle Scholar
  21. 21.
    Ribe AR, Laursen TM, Charles M, et al. Long-term risk of dementia in persons with schizophrenia: a Danish population-based cohort study. JAMA Psychiat. 2015;72(11):1095–101.CrossRefGoogle Scholar
  22. 22.
    Arts B, Jabben N, Krabbendam L, van Os J. Meta-analyses of cognitive functioning in euthymic bipolar patients and their first-degree relatives. Psychol Med. 2008;38(6):771–85.CrossRefPubMedGoogle Scholar
  23. 23.
    Almeida OP, McCaul K, Hankey GJ, Yeap BB, Golledge J, Flicker L. Risk of dementia and death in community-dwelling older men with bipolar disorder. Br J Psychiatry J Ment Sci. 2016;209(2):121–6.CrossRefGoogle Scholar
  24. 24.
    Forlenza OV, Aprahamian I, Radanovic M, et al. Cognitive impairment in late-life bipolar disorder is not associated with Alzheimer’s disease pathological signature in the cerebrospinal fluid. Bipolar Disord. 2016;18(1):63–70.CrossRefPubMedGoogle Scholar
  25. 25.
    Marshall V, Grosset D. Role of dopamine transporter imaging in routine clinical practice. Mov Disord. 2003;18(12):1415–23.CrossRefPubMedGoogle Scholar
  26. 26.
    Todorova A, Jenner P, Ray Chaudhuri K. Non-motor Parkinson’s: integral to motor Parkinson’s, yet often neglected. Pract Neurol. 2014;14(5):310–22.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Dubois B, Feldman HH, Jacova C, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 2007;6(8):734–46.CrossRefPubMedGoogle Scholar
  28. 28.
    McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):263–9.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Van Essen DC, Ugurbil K, Auerbach E, et al. The human Connectome project: a data acquisition perspective. NeuroImage. 2012;62(4):2222–31.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Teipel S, Drzezga A, Grothe MJ, et al. Multimodal imaging in Alzheimer’s disease: validity and usefulness for early detection. Lancet Neurol. 2015;14(10):1037–53.CrossRefPubMedGoogle Scholar
  31. 31.
    Sporns O, Tononi G, Kotter R. The human connectome: a structural description of the human brain. PLoS Comput Biol. 2005;1(4):e42.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Yaffe K. Moving beyond dualism to advance geriatric neuropsychiatry. Am J Geriatr Psychiatry. 2016;24(5):339–41.CrossRefPubMedGoogle Scholar
  33. 33.
    Gean AD, Kates RS, Lee S. Neuroimaging in head injury. New Horiz. 1995;3(3):549–61.PubMedGoogle Scholar
  34. 34.
    Parizel PM, Van Goethem JW, Ozsarlak O, Maes M, Phillips CD. New developments in the neuroradiological diagnosis of craniocerebral trauma. Eur Radiol. 2005;15(3):569–81.CrossRefPubMedGoogle Scholar
  35. 35.
    Chakraborty A, de Wit NM, van der Flier WM, de Vries HE. The blood brain barrier in Alzheimer’s disease. Vasc Pharmacol. 2016;89:12–8.CrossRefGoogle Scholar
  36. 36.
    Bowman GL, Kaye JA, Moore M, Waichunas D, Carlson NE, Quinn JF. Blood-brain barrier impairment in Alzheimer disease: stability and functional significance. Neurology. 2007;68(21):1809–14.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Zhang CE, Wong SM, van de Haar HJ, et al. Blood-brain barrier leakage is more widespread in patients with cerebral small vessel disease. Neurology. 2017;88(5):426–32.CrossRefPubMedGoogle Scholar
  38. 38.
    Wang CL, Cohan RH, Ellis JH, Caoili EM, Wang G, Francis IR. Frequency, outcome, and appropriateness of treatment of nonionic iodinated contrast media reactions. AJR Am J Roentgenol. 2008;191(2):409–15.CrossRefPubMedGoogle Scholar
  39. 39.
    Katayama H, Yamaguchi K, Kozuka T, Takashima T, Seez P, Matsuura K. Adverse reactions to ionic and nonionic contrast media. A report from the Japanese committee on the safety of contrast media. Radiology. 1990;175(3):621–8.CrossRefPubMedGoogle Scholar
  40. 40.
    Meth MJ, Maibach HI. Current understanding of contrast media reactions and implications for clinical management. Drug Saf. 2006;29(2):133–41.CrossRefPubMedGoogle Scholar
  41. 41.
    Kanal E, Maravilla K, Rowley HA. Gadolinium contrast agents for CNS imaging: current concepts and clinical evidence. AJNR Am J Neuroradiol. 2014;35(12):2215–26.CrossRefPubMedGoogle Scholar
  42. 42.
    Saba L. Imaging in neurodegenerative disorders. 1st ed. Oxford: Oxford University Press; 2015.Google Scholar
  43. 43.
    McCollough CH, Schueler BA. Calculation of effective dose. Med Phys. 2000;27(5):828–37.CrossRefPubMedGoogle Scholar
  44. 44.
    Gerber TC, Carr JJ, Arai AE, et al. Ionizing radiation in cardiac imaging: a science advisory from the American Heart Association Committee on cardiac imaging of the council on clinical cardiology and committee on cardiovascular imaging and intervention of the council on cardiovascular radiology and intervention. Circulation. 2009;119(7):1056–65.CrossRefPubMedGoogle Scholar
  45. 45.
    Balchandani P, Naidich TP. Ultra-high-field MR neuroimaging. AJNR Am J Neuroradiol. 2015;36(7):1204–15.CrossRefPubMedGoogle Scholar
  46. 46.
    Chavhan GB, Babyn PS, Thomas B, Shroff MM, Haacke EM. Principles, techniques, and applications of T2*-based MR imaging and its special applications. Radiographics. 2009;29(5):1433–49.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Westbrook C, Roth CK, Talbot J. MRI in practice. 3rd ed. Malden, MA: Blackwell Pub; 2005.Google Scholar
  48. 48.
    Barkhof F. Neuroimaging in dementia. Heidelberg, New York: Springer; 2011.CrossRefGoogle Scholar
  49. 49.
    Ramalho J, Semelka RC, Ramalho M, Nunes RH, AlObaidy M, Castillo M. Gadolinium-based contrast agent accumulation and toxicity: an update. AJNR Am J Neuroradiol. 2016;37(7):1192–8.CrossRefPubMedGoogle Scholar
  50. 50.
    Runge VM. Safety of magnetic resonance contrast media. Top Magn Reson Imaging. 2001;12(4):309–14.CrossRefPubMedGoogle Scholar
  51. 51.
    Marckmann P, Skov L, Rossen K, et al. Nephrogenic systemic fibrosis: suspected causative role of gadodiamide used for contrast-enhanced magnetic resonance imaging. J Am Soc Nephrol. 2006;17(9):2359–62.CrossRefPubMedGoogle Scholar
  52. 52.
    Choyke PL, Girton ME, Vaughan EM, Frank JA, Austin HA 3rd. Clearance of gadolinium chelates by hemodialysis: an in vitro study. J Magn Reson Imaging. 1995;5(4):470–2.CrossRefPubMedGoogle Scholar
  53. 53.
    Harper L, Barkhof F, Fox NC, Schott JM. Using visual rating to diagnose dementia: a critical evaluation of MRI atrophy scales. J Neurol Neurosurg Psychiatry. 2015;86(11):1225–33.CrossRefPubMedGoogle Scholar
  54. 54.
    Schmahmann JD. Vascular syndromes of the thalamus. Stroke. 2003;34(9):2264–78.CrossRefPubMedGoogle Scholar
  55. 55.
    Bastos Leite AJ, van Straaten EC, Scheltens P, Lycklama G, Barkhof F. Thalamic lesions in vascular dementia: low sensitivity of fluid-attenuated inversion recovery (FLAIR) imaging. Stroke. 2004;35(2):415–9.CrossRefPubMedGoogle Scholar
  56. 56.
    Al-Saeed O, Ismail M, Athyal RP, Rudwan M, Khafajee S. T1-weighted fluid-attenuated inversion recovery and T1-weighted fast spin-echo contrast-enhanced imaging: a comparison in 20 patients with brain lesions. J Med Imaging Radiat Oncol. 2009;53(4):366–72.CrossRefPubMedGoogle Scholar
  57. 57.
    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(6):1512–20.PubMedGoogle Scholar
  58. 58.
    Traboulsee A, Simon JH, Stone L, et al. Revised recommendations of the consortium of MS centers task force for a standardized MRI protocol and clinical guidelines for the diagnosis and follow-up of multiple sclerosis. AJNR Am J Neuroradiol. 2016;37(3):394–401.CrossRefPubMedGoogle Scholar
  59. 59.
    Shams S, Martola J, Cavallin L, et al. SWI or T2*: which MRI sequence to use in the detection of cerebral microbleeds? The Karolinska imaging dementia study. AJNR Am J Neuroradiol. 2015;36(6):1089–95.CrossRefPubMedGoogle Scholar
  60. 60.
    Robinson RJ, Bhuta S. Susceptibility-weighted imaging of the brain: current utility and potential applications. J Neuroimaging. 2011;21(4):e189–204.CrossRefPubMedGoogle Scholar
  61. 61.
    Finelli PF. Diagnostic approach to restricted-diffusion patterns on MR imaging. Neurology. 2012;2(4):287–93.Google Scholar
  62. 62.
    Deichmann R, Good CD, Josephs O, Ashburner J, Turner R. Optimization of 3-D MP-RAGE sequences for structural brain imaging. NeuroImage. 2000;12(1):112–27.CrossRefPubMedGoogle Scholar
  63. 63.
    Jack CR Jr, Bernstein MA, Fox NC, et al. The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–91.CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Mugler JP 3rd. Optimized three-dimensional fast-spin-echo MRI. J Magn Reson Imaging. 2014;39(4):745–67.CrossRefPubMedGoogle Scholar
  65. 65.
    Yousry TA, Schmid UD, Alkadhi H, et al. Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. Brain. 1997;120(Pt 1):141–57.CrossRefPubMedGoogle Scholar
  66. 66.
    Szabo K, Forster A, Gass A. Conventional and diffusion-weighted MRI of the hippocampus. Front Neurol Neurosci. 2014;34:71–84.CrossRefPubMedGoogle Scholar
  67. 67.
    Scheltens PH. Structural neuroimaging of Alzheimer’s disease and other dementias. Aging (Milano). 2001;13(3):203–9.Google Scholar
  68. 68.
    Clarfield AM. The decreasing prevalence of reversible dementias: an updated meta-analysis. Arch Intern Med. 2003;163(18):2219–29.CrossRefPubMedGoogle Scholar
  69. 69.
    Suarez J, Tartaglia MC, Vitali P, et al. Characterizing radiology reports in patients with frontotemporal dementia. Neurology. 2009;73(13):1073–4.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Harper L, Barkhof F, Scheltens P, Schott JM, Fox NC. An algorithmic approach to structural imaging in dementia. J Neurol Neurosurg Psychiatry. 2014;85(6):692–8.CrossRefPubMedGoogle Scholar
  71. 71.
    Logue MW, Posner H, Green RC, et al. Magnetic resonance imaging-measured atrophy and its relationship to cognitive functioning in vascular dementia and Alzheimer’s disease patients. Alzheimers Dement. 2011;7(5):493–500.CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Arba F, Quinn T, Hankey GJ, Ali M, Lees KR, Inzitari D. Cerebral small vessel disease, medial temporal lobe atrophy and cognitive status in patients with ischaemic stroke and transient ischaemic attack. Eur J Neurol. 2017;24(2):276–82.CrossRefPubMedGoogle Scholar
  73. 73.
    Firbank MJ, Burton EJ, Barber R, et al. Medial temporal atrophy rather than white matter hyperintensities predict cognitive decline in stroke survivors. Neurobiol Aging. 2007;28(11):1664–9.CrossRefPubMedGoogle Scholar
  74. 74.
    Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822–38.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Scheltens P, Barkhof F, Leys D, et al. A semiquantitative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. J Neurol Sci. 1993;114(1):7–12.CrossRefPubMedGoogle Scholar
  76. 76.
    Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol. 1987;149(2):351–6.CrossRefPubMedGoogle Scholar
  77. 77.
    Wahlund LO, Barkhof F, Fazekas F, et al. A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke. 2001;32(6):1318–22.CrossRefPubMedGoogle Scholar
  78. 78.
    Brewer JB. Fully-automated volumetric MRI with normative ranges: translation to clinical practice. Behav Neurol. 2009;21(1):21–8.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Bazin PL, Cuzzocreo JL, Yassa MA, et al. Volumetric neuroimage analysis extensions for the MIPAV software package. J Neurosci Methods. 2007;165(1):111–21.CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Filippi M, Agosta F, Barkhof F, et al. EFNS task force: the use of neuroimaging in the diagnosis of dementia. Eur J Neurol. 2012;19(12):e131–40, 1487–1501.CrossRefPubMedGoogle Scholar
  81. 81.
    Frisoni GB, Jack CR. HarP: the EADC-ADNI harmonized protocol for manual hippocampal segmentation. A standard of reference from a global working group. Alzheimers Dement. 2015;11(2):107–10.CrossRefPubMedGoogle Scholar
  82. 82.
    Bocchetta M, Boccardi M, Ganzola R, et al. Harmonized benchmark labels of the hippocampus on magnetic resonance: the EADC-ADNI project. Alzheimers Dement. 2015;11(2):151–160.e155.CrossRefPubMedGoogle Scholar
  83. 83.
    Davison CM, O’Brien JT. A comparison of FDG-PET and blood flow SPECT in the diagnosis of neurodegenerative dementias: a systematic review. Int J Geriatr Psychiatry. 2014;29(6):551–61.CrossRefPubMedGoogle Scholar
  84. 84.
    Archer HA, Smailagic N, John C, et al. Regional cerebral blood flow single photon emission computed tomography for detection of Frontotemporal dementia in people with suspected dementia. Cochrane Database Syst Rev. 2015;6:CD010896.Google Scholar
  85. 85.
    Bamford C, Olsen K, Davison C, et al. Is there a preference for PET or SPECT brain imaging in diagnosing dementia? The views of people with dementia, carers, and healthy controls. Int Psychogeriatr. 2016;28(1):123–31.CrossRefPubMedGoogle Scholar
  86. 86.
    Varrone A, Asenbaum S, Vander Borght T, et al. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging. 2009;36(12):2103–10.CrossRefPubMedGoogle Scholar
  87. 87.
    Attwell D, Iadecola C. The neural basis of functional brain imaging signals. Trends Neurosci. 2002;25(12):621–5.CrossRefPubMedGoogle Scholar
  88. 88.
    Meltzer CC, Zubieta JK, Brandt J, Tune LE, Mayberg HS, Frost JJ. Regional hypometabolism in Alzheimer’s disease as measured by positron emission tomography after correction for effects of partial volume averaging. Neurology. 1996;47(2):454–61.CrossRefPubMedGoogle Scholar
  89. 89.
    Pardo JV, Lee JT, Sheikh SA, et al. Where the brain grows old: decline in anterior cingulate and medial prefrontal function with normal aging. NeuroImage. 2007;35(3):1231–7.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Berti V, Mosconi L, Pupi A. Brain: normal variations and benign findings in fluorodeoxyglucose-PET/computed tomography imaging. PET Clin. 2014;9(2):129–40.CrossRefPubMedGoogle Scholar
  91. 91.
    Lameka K, Farwell MD, Ichise M. Positron emission tomography. Handb Clin Neurol. 2016;135:209–27.CrossRefPubMedGoogle Scholar
  92. 92.
    Huang B, Law MW, Khong PL. Whole-body PET/CT scanning: estimation of radiation dose and cancer risk. Radiology. 2009;251(1):166–74.CrossRefPubMedGoogle Scholar
  93. 93.
    Jagust W, Reed B, Mungas D, Ellis W, Decarli C. What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia? Neurology. 2007;69(9):871–7.CrossRefPubMedGoogle Scholar
  94. 94.
    Silverman DH, Small GW, Phelps ME. Clinical value of neuroimaging in the diagnosis of dementia. Sensitivity and specificity of regional cerebral metabolic and other parameters for early identification of Alzheimer’s disease. Clin Positron Imaging. 1999;2(3):119–30.CrossRefPubMedGoogle Scholar
  95. 95.
    Torosyan N, Silverman DH. Neuronuclear imaging in the evaluation of dementia and mild decline in cognition. Semin Nucl Med. 2012;42(6):415–22.CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Schlemmer H-PW, Pichler BJ, Schmand M, et al. Simultaneous MR/PET imaging of the human brain: feasibility study. Radiology. 2008;248(3):1028–35.CrossRefPubMedGoogle Scholar
  97. 97.
    Drzezga A, Souvatzoglou M, Eiber M, et al. First clinical experience with integrated whole-body PET/MR: comparison to PET/CT in patients with oncologic diagnoses. J Nucl Med. 2012;53(6):845–55.CrossRefPubMedGoogle Scholar
  98. 98.
    Catana C, Drzezga A, Heiss WD, Rosen BR. PET/MRI for neurologic applications. J Nucl Med. 2012;53(12):1916–25.CrossRefPubMedGoogle Scholar
  99. 99.
    Bisdas S, Nagele T, Schlemmer HP, et al. Switching on the lights for real-time multimodality tumor neuroimaging: the integrated positron-emission tomography/MR imaging system. AJNR Am J Neuroradiol. 2010;31(4):610–4.CrossRefPubMedGoogle Scholar
  100. 100.
    Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med. 2007;48(6):932–45.CrossRefPubMedGoogle Scholar
  101. 101.
    Kawasaki K, Ishii K, Saito Y, Oda K, Kimura Y, Ishiwata K. Influence of mild hyperglycemia on cerebral FDG distribution patterns calculated by statistical parametric mapping. Ann Nucl Med. 2008;22(3):191–200.CrossRefPubMedGoogle Scholar
  102. 102.
    Ishibashi K, Onishi A, Fujiwara Y, Ishiwata K, Ishii K. Relationship between Alzheimer disease-like pattern of 18F-FDG and fasting plasma glucose levels in cognitively normal volunteers. J Nucl Med. 2015;56(2):229–33.CrossRefPubMedGoogle Scholar
  103. 103.
    Brown RK, Bohnen NI, Wong KK, Minoshima S, Frey KA. Brain PET in suspected dementia: patterns of altered FDG metabolism. Radiographics. 2014;34(3):684–701.CrossRefPubMedGoogle Scholar
  104. 104.
    Silverman D. PET in the evaluation of Alzheimer’s disease and related disorders. Dordrecht: Springer; 2009.CrossRefGoogle Scholar
  105. 105.
    Silverman DH, Small GW, Chang CY, et al. Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA. 2001;286(17):2120–7.CrossRefPubMedGoogle Scholar
  106. 106.
    Yamane T, Ikari Y, Nishio T, et al. Visual-statistical interpretation of (18)F-FDG-PET images for characteristic Alzheimer patterns in a multicenter study: inter-rater concordance and relationship to automated quantitative evaluation. AJNR Am J Neuroradiol. 2014;35(2):244–9.CrossRefPubMedGoogle Scholar
  107. 107.
    Lehman VT, Carter RE, Claassen DO, et al. Visual assessment versus quantitative three-dimensional stereotactic surface projection fluorodeoxyglucose positron emission tomography for detection of mild cognitive impairment and Alzheimer disease. Clin Nucl Med. 2012;37(8):721–6.CrossRefPubMedGoogle Scholar
  108. 108.
    Matias-Guiu JA, Cabrera-Martin MN, Perez-Castejon MJ, et al. Visual and statistical analysis of (1)(8)F-FDG PET in primary progressive aphasia. Eur J Nucl Med Mol Imaging. 2015;42(6):916–27.CrossRefPubMedGoogle Scholar
  109. 109.
    Signorini M, Paulesu E, Friston K, et al. Rapid assessment of regional cerebral metabolic abnormalities in single subjects with quantitative and nonquantitative [18F]FDG PET: a clinical validation of statistical parametric mapping. NeuroImage. 1999;9(1):63–80.CrossRefPubMedGoogle Scholar
  110. 110.
    Minoshima S, Frey KA, Koeppe RA, Foster NL, Kuhl DE. A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med. 1995;36(7):1238–48.PubMedGoogle Scholar
  111. 111.
    Ashburner JT, Kiebel SJ, Nichols TE, Penny WD, Friston KJ. Statistical parametric mapping the analysis of functional brain images. Burlington: Elsevier Science; 2011.Google Scholar
  112. 112.
    Yakushev I, Landvogt C, Buchholz HG, et al. Choice of reference area in studies of Alzheimer’s disease using positron emission tomography with fluorodeoxyglucose-F18. Psychiatry Res. 2008;164(2):143–53.CrossRefPubMedGoogle Scholar
  113. 113.
    Dukart J, Mueller K, Horstmann A, et al. Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies. NeuroImage. 2010;49(2):1490–5.CrossRefPubMedGoogle Scholar
  114. 114.
    Geyer S, Schleicher A, Zilles K. Areas 3a, 3b, and 1 of human primary somatosensory cortex. NeuroImage. 1999;10(1):63–83.CrossRefPubMedGoogle Scholar
  115. 115.
    Moeller JR, Ishikawa T, Dhawan V, et al. The metabolic topography of normal aging. J Cereb Blood Flow Metab. 1996;16(3):385–98.CrossRefPubMedGoogle Scholar
  116. 116.
    Fukutani Y, Cairns NJ, Rossor MN, Lantos PL. Cerebellar pathology in sporadic and familial Alzheimer’s disease including APP 717 (Val-->Ile) mutation cases: a morphometric investigation. J Neurol Sci. 1997;149(2):177–84.CrossRefPubMedGoogle Scholar
  117. 117.
    Kushner M, Tobin M, Alavi A, et al. Cerebellar glucose consumption in normal and pathologic states using fluorine-FDG and PET. J Nucl Med. 1987;28(11):1667–70.PubMedGoogle Scholar
  118. 118.
    Akiyama H, Harrop R, McGeer PL, Peppard R, McGeer EG. Crossed cerebellar and uncrossed basal ganglia and thalamic diaschisis in Alzheimer’s disease. Neurology. 1989;39(4):541–8.CrossRefPubMedGoogle Scholar
  119. 119.
    von Monakow C. Die Lokalisation im Grosshirn und der Abbau der Funktion durch kortikale Herde. Wiesbaden: JF Bergmann; 1914.Google Scholar
  120. 120.
    Ritz L, Segobin S, Lannuzel C, et al. Direct voxel-based comparisons between grey matter shrinkage and glucose hypometabolism in chronic alcoholism. J Cereb Blood Flow Metab. 2016;36(9):1625–40.CrossRefPubMedGoogle Scholar
  121. 121.
    Akdemir UO, Tokcaer AB, Karakus A, Kapucu LO. Brain 18F-FDG PET imaging in the differential diagnosis of parkinsonism. Clin Nucl Med. 2014;39(3):e220–6.CrossRefPubMedGoogle Scholar
  122. 122.
    Suarez-Calvet M, Camacho V, Gomez-Anson B, et al. Early cerebellar Hypometabolism in patients with Frontotemporal dementia carrying the C9orf72 expansion. Alzheimer Dis Assoc Disord. 2015;29(4):353–6.CrossRefPubMedGoogle Scholar
  123. 123.
    Kwong KK, Belliveau JW, Chesler DA, et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A. 1992;89(12):5675–9.CrossRefPubMedPubMedCentralGoogle Scholar
  124. 124.
    Ogawa S, Tank DW, Menon R, et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci U S A. 1992;89(13):5951–5.CrossRefPubMedPubMedCentralGoogle Scholar
  125. 125.
    Buchbinder BR. Functional magnetic resonance imaging. Handb Clin Neurol. 2016;135:61–92.CrossRefPubMedGoogle Scholar
  126. 126.
    Bobholz JA, Rao SM, Saykin AJ, Pliskin N. Clinical use of functional magnetic resonance imaging: reflections on the new CPT codes. Neuropsychol Rev. 2007;17(2):189–91.CrossRefPubMedPubMedCentralGoogle Scholar
  127. 127.
    Official position of the division of clinical neuropsychology (APA division 40) on the role of neuropsychologists in clinical use of fMri: approved by the division 40 executive committee July 28, 2004. Clin Neuropsychol. 2004;18(3):349–51.Google Scholar
  128. 128.
    Buxton RB. Interpreting oxygenation-based neuroimaging signals: the importance and the challenge of understanding brain oxygen metabolism. Front Neuroenerg. 2010;2:8.Google Scholar
  129. 129.
    Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8(9):700–11.CrossRefPubMedGoogle Scholar
  130. 130.
    Damoiseaux JS, Rombouts SA, Barkhof F, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A. 2006;103(37):13848–53.CrossRefPubMedPubMedCentralGoogle Scholar
  131. 131.
    Biswal BB, Mennes M, Zuo XN, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A. 2010;107(10):4734–9.CrossRefPubMedPubMedCentralGoogle Scholar
  132. 132.
    Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98(2):676–82.CrossRefPubMedPubMedCentralGoogle Scholar
  133. 133.
    Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJS. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev. 2009;33(3):279–96.CrossRefPubMedGoogle Scholar
  134. 134.
    Doria V, Beckmann CF, Arichi T, et al. Emergence of resting state networks in the preterm human brain. Proc Natl Acad Sci U S A. 2010;107(46):20015–20.CrossRefPubMedPubMedCentralGoogle Scholar
  135. 135.
    Jolles DD, van Buchem MA, Crone EA, Rombouts SA. A comprehensive study of whole-brain functional connectivity in children and young adults. Cerebral Cortex. 2011;21(2):385–91.CrossRefPubMedGoogle Scholar
  136. 136.
    Bassett DS, Nelson BG, Mueller BA, Camchong J, Lim KO. Altered resting state complexity in schizophrenia. NeuroImage. 2012;59(3):2196–207.CrossRefPubMedGoogle Scholar
  137. 137.
    Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiat. 2015;72(6):603–11.CrossRefGoogle Scholar
  138. 138.
    Posner J, Park C, Wang Z. Connecting the dots: a review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder. Neuropsychol Rev. 2014;24(1):3–15.CrossRefPubMedPubMedCentralGoogle Scholar
  139. 139.
    Zhou J, Seeley WW. Network dysfunction in Alzheimer’s disease and frontotemporal dementia: implications for psychiatry. Biol Psychiatry. 2014;75(7):565–73.CrossRefPubMedGoogle Scholar
  140. 140.
    Loane C, Politis M. Positron emission tomography neuroimaging in Parkinson’s disease. Am J Transl Res. 2011;3(4):323–41.PubMedPubMedCentralGoogle Scholar
  141. 141.
    Darcourt J, Booij J, Tatsch K, et al. EANM procedure guidelines for brain neurotransmission SPECT using (123)I-labelled dopamine transporter ligands, version 2. Eur J Nucl Med Mol Imaging. 2010;37(2):443–50.CrossRefPubMedGoogle Scholar
  142. 142.
    Bajaj N, Hauser RA, Grachev ID. Clinical utility of dopamine transporter single photon emission CT (DaT-SPECT) with (123I) ioflupane in diagnosis of parkinsonian syndromes. J Neurol Neurosurg Psychiatry. 2013;84(11):1288–95.CrossRefPubMedPubMedCentralGoogle Scholar
  143. 143.
    Covington MF, McMillan NA, Avery RJ, Kuo PH. The semicolon sign: dopamine transporter imaging artifact from head tilt. J Nucl Med Technol. 2013;41(2):105–7.CrossRefPubMedGoogle Scholar
  144. 144.
    Kahraman D, Eggers C, Schicha H, Timmermann L, Schmidt M. Visual assessment of dopaminergic degeneration pattern in 123I-FP-CIT SPECT differentiates patients with atypical parkinsonian syndromes and idiopathic Parkinson’s disease. J Neurol. 2012;259(2):251–60.CrossRefPubMedGoogle Scholar
  145. 145.
    Davidsson A, Georgiopoulos C, Dizdar N, Granerus G, Zachrisson H. Comparison between visual assessment of dopaminergic degeneration pattern and semi-quantitative ratio calculations in patients with Parkinson’s disease and atypical Parkinsonian syndromes using DaTSCAN(R) SPECT. Ann Nucl Med. 2014;28(9):851–9.CrossRefPubMedGoogle Scholar
  146. 146.
    Kahraman D, Eggers C, Holstein A, et al. 123I-FP-CIT SPECT imaging of the dopaminergic state. Visual assessment of dopaminergic degeneration patterns reflects quantitative 2D operator-dependent and 3D operator-independent techniques. Nuklearmedizin. 2012;51(6):244–51.CrossRefPubMedGoogle Scholar
  147. 147.
    Van Laere K, Varrone A, Booij J, et al. EANM procedure guidelines for brain neurotransmission SPECT/PET using dopamine D2 receptor ligands, version 2. Eur J Nucl Med Mol Imaging. 2010;37(2):434–42.CrossRefPubMedGoogle Scholar
  148. 148.
    Badiavas K, Molyvda E, Iakovou I, Tsolaki M, Psarrakos K, Karatzas N. SPECT imaging evaluation in movement disorders: far beyond visual assessment. Eur J Nucl Med Mol Imaging. 2011;38(4):764–73.CrossRefPubMedGoogle Scholar
  149. 149.
    Booij J, Tissingh G, Winogrodzka A, et al. Practical benefit of [123I]FP-CIT SPET in the demonstration of the dopaminergic deficit in Parkinson’s disease. Eur J Nucl Med. 1997;24(1):68–71.CrossRefPubMedGoogle Scholar
  150. 150.
    van Dyck CH, Seibyl JP, Malison RT, et al. Age-related decline in dopamine transporters: analysis of striatal subregions, nonlinear effects, and hemispheric asymmetries. Am J Geriatr Psychiatry. 2002;10(1):36–43.CrossRefPubMedGoogle Scholar
  151. 151.
    DeSantis J, Sun S. Quantitative assessment of DaTQUANT in diagnosis of DaTscan patients. J Nucl Med. 2013;54(2_MeetingAbstracts):2705.Google Scholar
  152. 152.
    Varrone A, Dickson JC, Tossici-Bolt L, et al. European multicentre database of healthy controls for [123I]FP-CIT SPECT (ENC-DAT): age-related effects, gender differences and evaluation of different methods of analysis. Eur J Nucl Med Mol Imaging. 2013;40(2):213–27.CrossRefPubMedGoogle Scholar
  153. 153.
    Oravivattanakul S, Benchaya L, Wu G, et al. Dopamine transporter (DaT) scan utilization in a movement disorder center. Mov Disord Clin Pract. 2016;3(1):31–5.CrossRefGoogle Scholar
  154. 154.
    Kagi G, Bhatia KP, Tolosa E. The role of DAT-SPECT in movement disorders. J Neurol Neurosurg Psychiatry. 2010;81(1):5–12.CrossRefPubMedGoogle Scholar
  155. 155.
    Tolosa E, Coelho M, Gallardo M. DAT imaging in drug-induced and psychogenic parkinsonism. Mov Disord. 2003;18(Suppl 7):S28–33.CrossRefPubMedGoogle Scholar
  156. 156.
    McKeith IG, Dickson DW, Lowe J, et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium. Neurology. 2005;65(12):1863–72.CrossRefPubMedGoogle Scholar
  157. 157.
    Nelson PT, Jicha GA, Kryscio RJ, et al. Low sensitivity in clinical diagnoses of dementia with Lewy bodies. J Neurol. 2010;257(3):359–66.CrossRefPubMedGoogle Scholar
  158. 158.
    Walker Z, Jaros E, Walker RW, et al. Dementia with Lewy bodies: a comparison of clinical diagnosis, FP-CIT single photon emission computed tomography imaging and autopsy. J Neurol Neurosurg Psychiatry. 2007;78(11):1176–81.CrossRefPubMedPubMedCentralGoogle Scholar
  159. 159.
    Thomas AJ, Attems J, Colloby SJ, et al. Autopsy validation of 123I-FP-CIT dopaminergic neuroimaging for the diagnosis of DLB. Neurology. 2017;88(3):276–83.CrossRefPubMedPubMedCentralGoogle Scholar
  160. 160.
    Walker Z, Moreno E, Thomas A, et al. Clinical usefulness of dopamine transporter SPECT imaging with 123I-FP-CIT in patients with possible dementia with Lewy bodies: randomised study. Br J Psychiatry J Ment Sci. 2015;206(2):145–52.CrossRefGoogle Scholar
  161. 161.
    Walker Z, Moreno E, Thomas A, et al. Evolution of clinical features in possible DLB depending on FP-CIT SPECT result. Neurology. 2016;87(10):1045–51.CrossRefPubMedPubMedCentralGoogle Scholar
  162. 162.
    Morgan S, Kemp P, Booij J, et al. Differentiation of frontotemporal dementia from dementia with Lewy bodies using FP-CIT SPECT. J Neurol Neurosurg Psychiatry. 2012;83(11):1063–70.CrossRefPubMedGoogle Scholar
  163. 163.
    O’Brien JT, Colloby S, Fenwick J, et al. Dopamine transporter loss visualized with FP-CIT SPECT in the differential diagnosis of dementia with Lewy bodies. Arch Neurol. 2004;61(6):919–25.CrossRefPubMedGoogle Scholar
  164. 164.
    Taki J, Yoshita M, Yamada M, Tonami N. Significance of 123I-MIBG scintigraphy as a pathophysiological indicator in the assessment of Parkinson's disease and related disorders: it can be a specific marker for Lewy body disease. Ann Nucl Med. 2004;18(6):453–61.CrossRefPubMedGoogle Scholar
  165. 165.
    Dae MW. Imaging of myocardial sympathetic innervation with metaiodobenzylguanidine. J Nucl Cardiol. 1994;1(2 Pt 2):S23–30.CrossRefPubMedGoogle Scholar
  166. 166.
    Takahashi M, Ikemura M, Oka T, et al. Quantitative correlation between cardiac MIBG uptake and remaining axons in the cardiac sympathetic nerve in Lewy body disease. J Neurol Neurosurg Psychiatry. 2015;86(9):939–44.CrossRefPubMedGoogle Scholar
  167. 167.
    Jost WH, Del Tredici K, Landvogt C, Braune S. Importance of 123I-metaiodobenzylguanidine scintigraphy/single photon emission computed tomography for diagnosis and differential diagnostics of Parkinson syndromes. Neurodegener Dis. 2010;7(5):341–7.CrossRefPubMedGoogle Scholar
  168. 168.
    Orimo S, Suzuki M, Inaba A, Mizusawa H. 123I-MIBG myocardial scintigraphy for differentiating Parkinson’s disease from other neurodegenerative parkinsonism: a systematic review and meta-analysis. Parkinsonism Relat Disord. 2012;18(5):494–500.CrossRefPubMedGoogle Scholar
  169. 169.
    Fanciulli A, Wenning GK. Multiple-system atrophy. N Engl J Med. 2015;372(3):249–63.CrossRefPubMedGoogle Scholar
  170. 170.
    Lucio CG, Vincenzo C, Antonio R, Oscar T, Antonio R, Luigi M. Neurological applications for myocardial MIBG scintigraphy. Nucl Med Rev Cent East Eur. 2013;16(1):35–41.CrossRefPubMedGoogle Scholar
  171. 171.
    Sakamoto F, Shiraishi S, Tsuda N, et al. 123I-MIBG myocardial scintigraphy for the evaluation of Lewy body disease: are delayed images essential? Is visual assessment useful? Br J Radiol. 2016;89(1064):20160144.CrossRefPubMedCentralGoogle Scholar
  172. 172.
    Yoshita M, Arai H, Arai H, et al. Diagnostic accuracy of 123I-meta-iodobenzylguanidine myocardial scintigraphy in dementia with Lewy bodies: a multicenter study. PLoS One. 2015;10(3):e0120540.CrossRefPubMedPubMedCentralGoogle Scholar
  173. 173.
    Spiegel J, Mollers MO, Jost WH, et al. FP-CIT and MIBG scintigraphy in early Parkinson’s disease. Mov Disord. 2005;20(5):552–61.CrossRefPubMedGoogle Scholar
  174. 174.
    Slaets S, Van Acker F, Versijpt J, et al. Diagnostic value of MIBG cardiac scintigraphy for differential dementia diagnosis. Int J Geriatr Psychiatry. 2015;30(8):864–9.CrossRefPubMedGoogle Scholar
  175. 175.
    Mascalchi M, Vella A, Ceravolo R. Movement disorders: role of imaging in diagnosis. J Magn Reson Imaging. 2012;35(2):239–56.CrossRefPubMedGoogle Scholar
  176. 176.
    Shimizu S, Hirao K, Kanetaka H, et al. Utility of the combination of DAT SPECT and MIBG myocardial scintigraphy in differentiating dementia with Lewy bodies from Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2016;43(1):184–92.CrossRefPubMedGoogle Scholar
  177. 177.
    Shimizu S, Kanetaka H, Hirao K, et al. Neuroimaging for diagnosing dementia with Lewy bodies: what is the best neuroimaging technique in discriminating dementia with Lewy bodies from Alzheimer’s disease? Geriatr Gerontol Int. 2017;17(5):819–24.CrossRefPubMedGoogle Scholar
  178. 178.
    Jack CR Jr, Knopman DS, Jagust WJ, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207–16.CrossRefPubMedPubMedCentralGoogle Scholar
  179. 179.
    Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science (New York, NY). 2002;297(5580):353–6.CrossRefGoogle Scholar
  180. 180.
    De-Paula VJ, Radanovic M, Diniz BS, Forlenza OV. Alzheimer’s disease. Subcell Biochem. 2012;65:329–52.CrossRefPubMedGoogle Scholar
  181. 181.
    Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med. 2011;1(1):a006189.CrossRefPubMedPubMedCentralGoogle Scholar
  182. 182.
    Rowe CC, Villemagne VL. Brain amyloid imaging. J Nucl Med. 2011;52(11):1733–40.PubMedGoogle Scholar
  183. 183.
    Hyman BT, Phelps CH, Beach TG, et al. National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement. 2012;8(1):1–13.CrossRefPubMedPubMedCentralGoogle Scholar
  184. 184.
    Savva GM, Wharton SB, Ince PG, Forster G, Matthews FE, Brayne C. Age, neuropathology, and dementia. N Engl J Med. 2009;360(22):2302–9.CrossRefPubMedGoogle Scholar
  185. 185.
    Ossenkoppele R, Jansen WJ, Rabinovici GD, et al. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA. 2015;313(19):1939–49.CrossRefPubMedPubMedCentralGoogle Scholar
  186. 186.
    Chetelat G, Ossenkoppele R, Villemagne VL, et al. Atrophy, hypometabolism and clinical trajectories in patients with amyloid-negative Alzheimer’s disease. Brain. 2016;139(Pt 9):2528–39.CrossRefPubMedGoogle Scholar
  187. 187.
    Klunk WE, Mathis CA. Whatever happened to Pittsburgh compound-a? Alzheimer Dis Assoc Disord. 2008;22(3):198–203.CrossRefPubMedPubMedCentralGoogle Scholar
  188. 188.
    Klunk WE, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh compound-B. Ann Neurol. 2004;55(3):306–19.CrossRefPubMedGoogle Scholar
  189. 189.
    Johnson KA, Minoshima S, Bohnen NI, et al. Update on appropriate use criteria for amyloid PET imaging: dementia experts, mild cognitive impairment, and education. Amyloid imaging task force of the Alzheimer’s Association and Society for Nuclear Medicine and Molecular Imaging. Alzheimers Dement. 2013;9(4):e106–9.CrossRefPubMedGoogle Scholar
  190. 190.
    Rowe CC, Ackerman U, Browne W, et al. Imaging of amyloid Beta in Alzheimer’s disease with 18F-BAY94-9172, a novel PET tracer: proof of mechanism. Lancet Neurol. 2008;7(2):129–35.CrossRefPubMedGoogle Scholar
  191. 191.
    Minoshima S, Drzezga AE, Barthel H, et al. SNMMI procedure standard/EANM practice guideline for amyloid PET imaging of the brain 1.0. J Nucl Med. 2016;57(8):1316–22.CrossRefPubMedGoogle Scholar
  192. 192.
    Johnson KA, Minoshima S, Bohnen NI, et al. Update on appropriate use criteria for amyloid PET imaging: dementia experts, mild cognitive impairment, and education. J Nucl Med. 2013;54(7):1011–3.CrossRefPubMedGoogle Scholar
  193. 193.
    Foster NL, Mottola K, Hoffman JM. Coverage with evidence development: what to consider. JAMA Neurol. 2014;71(4):399–400.CrossRefPubMedGoogle Scholar
  194. 194.
    IDEAS. Study opens registration. J Nucl Med. 2016;57(1):9N.CrossRefGoogle Scholar
  195. 195.
    Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N Engl J Med. 2014;370(4):322–33.CrossRefPubMedPubMedCentralGoogle Scholar
  196. 196.
    Mesulam MM, Weintraub S, Rogalski EJ, Wieneke C, Geula C, Bigio EH. Asymmetry and heterogeneity of Alzheimer’s and frontotemporal pathology in primary progressive aphasia. Brain. 2014;137(Pt 4):1176–92.CrossRefPubMedPubMedCentralGoogle Scholar
  197. 197.
    Shah M, Catafau AM. Molecular imaging insights into Neurodegeneration: focus on Tau PET radiotracers. J Nucl Med. 2014;55(6):871–4.CrossRefPubMedGoogle Scholar
  198. 198.
    Weiner MW, Veitch DP, Aisen PS, et al. The Alzheimer’s disease neuroimaging initiative 3: continued innovation for clinical trial improvement. Alzheimers Dement. 2017;13(5):561–71.CrossRefPubMedGoogle Scholar
  199. 199.
    Jack CR Jr, Bennett DA, Blennow K, et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87(5):539–47.CrossRefPubMedPubMedCentralGoogle Scholar
  200. 200.
    Shah M, Seibyl J, Cartier A, Bhatt R, Catafau AM. Molecular imaging insights into neurodegeneration: focus on alpha-synuclein radiotracers. J Nucl Med. 2014;55(9):1397–400.CrossRefPubMedGoogle Scholar
  201. 201.
    Zhang J, Sun J, Stahl JN. PACS and web-based image distribution and display. Comput Med Imaging Graph. 2003;27(2-3):197–206.CrossRefPubMedGoogle Scholar
  202. 202.
    Ratib O, Swiernik M, McCoy JM. From PACS to integrated EMR. Comput Med Imaging Graph. 2003;27(2-3):207–15.CrossRefPubMedGoogle Scholar
  203. 203.
    Global health and aging. Bethesda, Maryland: National Institute of Aging, National Institutes of Health, US. Department of Health and Human Services; 2011.Google Scholar
  204. 204.
    Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology. 2013;80(19):1778–83.CrossRefPubMedPubMedCentralGoogle Scholar
  205. 205.
    Hinton L, Franz CE, Reddy G, Flores Y, Kravitz RL, Barker JC. Practice constraints, behavioral problems, and dementia care: primary care physicians’ perspectives. J Gen Intern Med. 2007;22(11):1487–92.CrossRefPubMedPubMedCentralGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of NeurologyOregon Health and Science UniversityPortlandUSA

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