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Chapter 13 - Neuroimaging in the Perioperative Neurocognitive Disorders

from Section 3 - Symptomatology and Diagnosis for the Perioperative Neurocognitive Disorders

Published online by Cambridge University Press:  11 April 2019

By
James W. Ibinson
Edited by
Roderic G. Eckenhoff  and
Niccolò Terrando
Roderic G. Eckenhoff
Affiliation:
University of Pennsylvania
Niccolò Terrando
Affiliation:
Duke University, North Carolina
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Chapter
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The Perioperative Neurocognitive Disorders , pp. 152 - 166
Publisher: Cambridge University Press
Print publication year: 2019

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References

Glover, GH. Overview of functional magnetic resonance imaging. Neurosurg Clin N Am. 2011;22(2):133–9, vii.CrossRefGoogle ScholarPubMed
Ibinson, JW, Vogt, KM. Pain does not follow the boxcar model: temporal dynamics of the BOLD fMRI signal during constant current painful electric nerve stimulation. J Pain. 2013;14(12):1611–19.Google Scholar
Biswal, B, Yetkin, FZ, Haughton, VM, Hyde, JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34(4):537–41.CrossRefGoogle ScholarPubMed
van den Heuvel, MP, Hulshoff Pol, HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010;20(8):519–34.CrossRefGoogle ScholarPubMed
Drysdale, AT, Grosenick, L, Downar, J, Dunlop, K, Mansouri, F, Meng, Y, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23(1):28–38.CrossRefGoogle ScholarPubMed
Sheline, YI, Raichle, ME. Resting state functional connectivity in preclinical Alzheimer’s disease. Biol Psychiatry. 2013;74(5):340–7.CrossRefGoogle ScholarPubMed
Klunk, WE, Engler, H, Nordberg, A, Wang, Y, Blomqvist, G, Holt, DP, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306–19.Google Scholar
Ishii, K. PET approaches for diagnosis of dementia. AJNR Am J Neuroradiol. 2014;35(11):2030–8.Google Scholar
Zimmer, ER, Leuzy, A, Benedet, AL, Breitner, J, Gauthier, S, Rosa-Neto, P. Tracking neuroinflammation in Alzheimer’s disease: the role of positron emission tomography imaging. J Neuroinflammation. 2014;11:120.CrossRefGoogle ScholarPubMed
Chen, CW, Lin, CC, Chen, KB, Kuo, YC, Li, CY, Chung, CJ. Increased risk of dementia in people with previous exposure to general anesthesia: a nationwide population-based case-control study. Alzheimers Dement. 2014;10(2):196–204.CrossRefGoogle ScholarPubMed
Chen, PL, Yang, CW, Tseng, YK, Sun, WZ, Wang, JL, Wang, SJ, et al. Risk of dementia after anaesthesia and surgery. Br J Psychiatry. 2014;204(3):188–93.Google Scholar
Berger, M, Burke, J, Eckenhoff, R, Mathew, J. Alzheimer’s disease, anesthesia, and surgery: a clinically focused review. J Cardiothorac Vasc Anesth. 2014;28(6):1609–23.Google Scholar
Evered, L, Silbert, B, Scott, DA, Ames, D, Maruff, P, Blennow, K. Cerebrospinal fluid biomarker for Alzheimer disease predicts postoperative cognitive dysfunction. Anesthesiology. 2016;124(2):353–61.CrossRefGoogle ScholarPubMed
Coimbra, A, Williams, DS, Hostetler, ED. The role of MRI and PET/SPECT in Alzheimer’s disease. Curr Top Med Chem. 2006;6(6):629–47.Google Scholar
Mosconi, L, Berti, V, Glodzik, L, Pupi, A, De Santi, S, de Leon, MJ. Pre-clinical detection of Alzheimer’s disease using FDG-PET, with or without amyloid imaging. J Alzheimers Dis. 2010;20(3):843–54.CrossRefGoogle ScholarPubMed
Rabinovici, GD, Jagust, WJ. Amyloid imaging in aging and dementia: testing the amyloid hypothesis in vivo. Behav Neurol. 2009;21(1):117–28.CrossRefGoogle ScholarPubMed
Small, GW, Bookheimer, SY, Thompson, PM, Cole, GM, Huang, SC, Kepe, V, et al. Current and future uses of neuroimaging for cognitively impaired patients. Lancet Neurol. 2008;7(2):161–72.Google Scholar
Rowe, CC, Ellis, KA, Rimajova, M, Bourgeat, P, Pike, KE, Jones, G, et al. Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging. 2010;31(8):1275–83.Google Scholar
Hamelin, L, Lagarde, J, Dorothee, G, Leroy, C, Labit, M, Comley, RA, et al. Early and protective microglial activation in Alzheimer’s disease: a prospective study using 18F-DPA-714 PET imaging. Brain. 2016;139(Pt 4):1252–64.Google Scholar
Saidlitz, P, Voisin, T, Vellas, B, Payoux, P, Gabelle, A, Formaglio, M, et al. Amyloid imaging in Alzheimer’s disease: a literature review. J Nutr Health Aging. 2014;18(7):723–40.Google Scholar
Wang, L, Benzinger, TL, Su, Y, Christensen, J, Friedrichsen, K, Aldea, P, et al. Evaluation of tau imaging in staging Alzheimer disease and revealing interactions between beta-amyloid and tauopathy. JAMA Neurol. 2016;73(9):1070–7.Google Scholar
Hoenig, MC, Bischof, GN, Seemiller, J, Hammes, J, Kukolja, J, Onur, OA, et al. Networks of tau distribution in Alzheimer’s disease. Brain. 2018;141(2):568–81.Google Scholar
Kang, JM, Lee, SY, Seo, S, Jeong, HJ, Woo, SH, Lee, H, et al. Tau positron emission tomography using [(18)F]THK5351 and cerebral glucose hypometabolism in Alzheimer’s disease. Neurobiol Aging. 2017;59:210–19.CrossRefGoogle Scholar
Adlard, PA, Tran, BA, Finkelstein, DI, Desmond, PM, Johnston, LA, Bush, AI, et al. A review of beta-amyloid neuroimaging in Alzheimer’s disease. Front Neurosci. 2014;8:327.CrossRefGoogle ScholarPubMed
Glodzik-Sobanska, L, Rusinek, H, Mosconi, L, Li, Y, Zhan, J, de Santi, S, et al. The role of quantitative structural imaging in the early diagnosis of Alzheimer’s disease. Neuroimag Clin N Am. 2005;15(4):803–26, x.CrossRefGoogle ScholarPubMed
Ramani, A, Jensen, JH, Helpern, JA. Quantitative MR imaging in Alzheimer disease. Radiology. 2006;241(1):26–44.Google Scholar
Vemuri, P, Jack, CR Jr. Role of structural MRI in Alzheimer’s disease. Alzheimers Res Ther. 2010;2(4):23.Google Scholar
Frisoni, GB, Fox, NC, Jack, CR Jr, Scheltens, P, Thompson, PM. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol. 2010;6(2):67–77.CrossRefGoogle ScholarPubMed
Lehericy, S, Delmaire, C, Galanaud, D, Dormont, D. Neuroimaging in dementia. Presse Med. 2007;36(10 Pt 2):1453–63.Google Scholar
Huijbers, W, Mormino, EC, Schultz, AP, Wigman, S, Ward, AM, Larvie, M, et al. Amyloid-β deposition in mild cognitive impairment is associated with increased hippocampal activity, atrophy and clinical progression. Brain. 2015;138(4):1023–35.CrossRefGoogle ScholarPubMed
Allen, G, Barnard, H, McColl, R, Hester, AL, Fields, JA, Weiner, MF, et al. Reduced hippocampal functional connectivity in Alzheimer disease. Arch Neurol. 2007;64(10):1482–7.Google Scholar
Sheline, YI, Raichle, ME, Snyder, AZ, Morris, JC, Head, D, Wang, S, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry. 2010;67(6):584–7.CrossRefGoogle ScholarPubMed
Onoda, K, Yada, N, Ozasa, K, Hara, S, Yamamoto, Y, Kitagaki, H, et al. Can a resting-state functional connectivity index identify patients with Alzheimer’s disease and mild cognitive impairment across multiple sites? Brain Connect. 2017;Jun 30.Google Scholar
Eikelenboom, P, van Gool, WA. Neuroinflammatory perspectives on the two faces of Alzheimer’s disease. J Neural Transm (Vienna). 2004;111(3):281–94.Google Scholar
Griffin, WS, Stanley, LC, Ling, C, White, L, MacLeod, V, Perrot, LJ, et al. Brain interleukin 1 and S-100 immunoreactivity are elevated in Down syndrome and Alzheimer disease. Proc Natl Acad Sci USA. 1989;86(19):7611–15.Google Scholar
Alam, MM, Lee, J, Lee, SY. Recent progress in the development of TSPO PET ligands for neuroinflammation imaging in neurological diseases. Nucl Med Mol Imaging. 2017;51(4):283–96.CrossRefGoogle ScholarPubMed
Gerhard, A, Pavese, N, Hotton, G, Turkheimer, F, Es, M, Hammers, A, et al. In vivo imaging of microglial activation with [11C](R)-PK11195 PET in idiopathic Parkinson’s disease. Neurobiol Dis. 2006;21(2):404–12.Google Scholar
Jucaite, A, Svenningsson, P, Rinne, JO, Cselenyi, Z, Varnas, K, Johnstrom, P, et al. Effect of the myeloperoxidase inhibitor AZD3241 on microglia: a PET study in Parkinson’s disease. Brain. 2015;138(Pt 9):2687–700.CrossRefGoogle ScholarPubMed
Iaccarino, L, Sala, A, Caminiti, SP, Perani, D. The emerging role of PET imaging in dementia. F1000Res. 2017;6:1830.Google Scholar
Zhang, X, Paule, MG, Newport, GD, Liu, F, Callicott, R, Liu, S, et al. MicroPET/CT imaging of [18F]-FEPPA in the nonhuman primate: a potential biomarker of pathogenic processes associated with anesthetic-induced neurotoxicity. ISRN Anesthesiol. 2012;2012:11.Google Scholar
Forsberg, A, Cervenka, S, Jonsson Fagerlund, M, Rasmussen, LS, Zetterberg, H, Erlandsson Harris, H, et al. The immune response of the human brain to abdominal surgery. Ann Neurol. 2017;81(4):572–82.CrossRefGoogle ScholarPubMed
Vik, A, Brubakk, AO, Rinck, PA, Sande, E, Levang, OW, Sellevold, O. MRI: a method to detect minor brain damage following coronary bypass surgery? Neuroradiology. 1991;33(5):396–8.Google Scholar
Sun, X, Lindsay, J, Monsein, LH, Hill, PC, Corso, PJ. Silent brain injury after cardiac surgery: a review: cognitive dysfunction and magnetic resonance imaging diffusion-weighted imaging findings. J Am Coll Cardiol. 2012;60(9):791–7.Google Scholar
Gerriets, T, Schwarz, N, Bachmann, G, Kaps, M, Kloevekorn, WP, Sammer, G, et al. Evaluation of methods to predict early long-term neurobehavioral outcome after coronary artery bypass grafting. Am J Cardiol. 2010;105(8):1095–101.CrossRefGoogle ScholarPubMed
Knipp, SC, Matatko, N, Schlamann, M, Wilhelm, H, Thielmann, M, Forsting, M, et al. Small ischemic brain lesions after cardiac valve replacement detected by diffusion-weighted magnetic resonance imaging: relation to neurocognitive function. Eur J Cardiothorac Surg. 2005;28(1):88–96.Google Scholar
Knipp, SC, Matatko, N, Wilhelm, H, Schlamann, M, Thielmann, M, Losch, C, et al. Cognitive outcomes three years after coronary artery bypass surgery: relation to diffusion-weighted magnetic resonance imaging. Ann Thorac Surg. 2008;85(3):872–9.Google Scholar
Cook, DJ, Huston, J 3rd, Trenerry, MR, Brown, RD Jr, Zehr, KJ, Sundt, TM 3rd. Postcardiac surgical cognitive impairment in the aged using diffusion-weighted magnetic resonance imaging. Ann Thorac Surg. 2007;83(4):1389–95.Google Scholar
Kahlert, P, Knipp, SC, Schlamann, M, Thielmann, M, Al-Rashid, F, Weber, M, et al. Silent and apparent cerebral ischemia after percutaneous transfemoral aortic valve implantation: a diffusion-weighted magnetic resonance imaging study. Circulation. 2010;121(7):870–8.CrossRefGoogle ScholarPubMed
Scott, DA, Evered, LA, Gerraty, RP, MacIsaac, A, Lai-Kwon, J, Silbert, BS. Cognitive dysfunction follows left heart catheterisation but is not related to microembolic count. Int J Cardiol. 2014;175(1):67–71.CrossRefGoogle Scholar
Kruis, RW, Vlasveld, FA, Van Dijk, D. The (un)importance of cerebral microemboli. Semin Cardiothorac Vasc Anesth. 2010;14(2):111–18.Google Scholar
Xie, P, Yu, T, Fu, X, Tu, Y, Zou, Y, Lui, S, et al. Altered functional connectivity in an aged rat model of postoperative cognitive dysfunction: a study using resting-state functional MRI. PLoS One. 2013;8(5):e64820.CrossRefGoogle Scholar
Browndyke, JN, Berger, M, Harshbarger, TB, Smith, PJ, White, W, Bisanar, TL, et al. Resting-state functional connectivity and cognition after major cardiac surgery in older adults without preoperative cognitive impairment: preliminary findings. J Am Geriatr Soc. 2017;65(1):e6–e12.CrossRefGoogle ScholarPubMed
Browndyke, JN, Berger, M, Smith, PJ, Harshbarger, TB, Monge, ZA, Panchal, V, et al. Task-related changes in degree centrality and local coherence of the posterior cingulate cortex after major cardiac surgery in older adults. Hum Brain Mapp. 2017;39:985–1003.Google Scholar
Huang, H, Tanner, J, Parvataneni, H, Rice, M, Horgas, A, Ding, M, et al. Impact of total knee arthroplasty with general anesthesia on brain networks: cognitive efficiency and ventricular volume predict functional connectivity decline in older adults. J Alzheimers Dis. 2018;62:319–33.Google Scholar

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