Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity

Nature Communications

Volumn 8, Issue 1, 2017, Pages

  Palmqvist, Sebastian ^a,b   Schöll, Michael ^a,c   Strandberg, Olof ^a   Mattsson, Niklas ^a,b   Stomrud, Erik ^a,b   Zetterberg, Henrik ^d,e,f   Blennow, Kaj ^d,e   Landau, Susan ^g   Jagust, William ^g   Hansson, Oskar ^a,b  

^a LUND UNIVERSITY   (Sweden)

^b LUND UNIVERSITY HOSPITAL   (Sweden)

^c UNIVERSITY OF GOTHENBURG   (Sweden)

^d UNIVERSITY OF GOTHENBURG   (Sweden)

^e SAHLGRENSKA UNIVERSITY HOSPITAL   (Sweden)

^f UNIVERSITY COLLEGE LONDON   (United Kingdom)

^g UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMYLOID BETA PROTEIN; AMYLOID BETA PROTEIN[1-42]; BIOLOGICAL MARKER; GLUCOSE;

BRAIN; COHORT ANALYSIS; CONNECTIVITY; DETECTION METHOD; METABOLISM; NERVOUS SYSTEM DISORDER; PEPTIDE; PHYSIOLOGY;

ADULT; AGED; ALZHEIMER DISEASE; ARTICLE; BRAIN ATROPHY; BRAIN REGION; CEREBROSPINAL FLUID; CLINICAL DEMENTIA RATING; COMPARATIVE STUDY; DEFAULT MODE NETWORK; FEMALE; FOLLOW UP; FUNCTIONAL CONNECTIVITY; GLUCOSE METABOLISM; HUMAN; IMAGE PROCESSING; MAJOR CLINICAL STUDY; MALE; MEDIAL ORBITOFRONTAL CORTEX; MINI MENTAL STATE EXAMINATION; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; POSTERIOR CINGULATE; PRECUNEUS; ATROPHY; BRAIN; COHORT ANALYSIS; METABOLISM; NERVE CELL NETWORK; ORGAN SIZE; PATHOLOGY; PATHOPHYSIOLOGY; REPRODUCIBILITY;

AGED; ALZHEIMER DISEASE; AMYLOID BETA-PEPTIDES; ATROPHY; BRAIN; COHORT STUDIES; FEMALE; GLUCOSE; HUMANS; MALE; NERVE NET; ORGAN SIZE; REPRODUCIBILITY OF RESULTS;

EID: 85032661576     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-017-01150-x     Document Type: Article

References (74)

1. Jack, C. R. Jr. et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12, 207-216, (2013).
2. Sperling, R., Mormino, E. & Johnson, K. The evolution of preclinical Alzheimer's disease: implications for prevention trials. Neuron 84, 608-622 (2014).
3. Suva, D. et al. Primary motor cortex involvement in Alzheimer disease. J Neuropathol. Exp. Neurol. 58, 1125-1134 (1999).
4. Villain, N. et al. Regional dynamics of amyloid-beta deposition in healthy elderly, mild cognitive impairment and Alzheimer's disease: a voxelwise PiBPET longitudinal study. Brain 135, 2126-2139 (2012).
5. Thal, D. R., Rub, U., Orantes, M. & Braak, H. Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology 58, 1791-1800 (2002).
6. Benzinger, T. L. et al. Regional variability of imaging biomarkers in autosomal dominant Alzheimer's disease. Proc. Natl Acad. Sci. USA 110, E4502-4509 (2013).
7. Bateman, R. J. et al. Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N. Engl. J. Med. 367, 795-804 (2012).
8. Blennow, K., Zetterberg, H. & Fagan, A. M. Fluid biomarkers in Alzheimer disease. Cold Spring Harb. Perspect. Med. 2, a006221 (2012).
9. Fagan, A. M. et al. Cerebrospinal fluid tau and ptau(181) increase with cortical amyloid deposition in cognitively normal individuals: implications for future clinical trials of Alzheimer's disease. EMBO Mol. Med. 1, 371-380 (2009).
10. Mattsson, N. et al. Independent information from cerebrospinal fluid β-amyloid and florbetapir imaging in Alzheimer's disease. Brain 138, 772-783 (2014).
11. Morris, J. C. et al. APOE predicts amyloid-beta but not tau Alzheimer pathology in cognitively normal aging. Ann. Neurol. 67, 122-131 (2010).
12. Palmqvist, S., Mattsson, N., Hansson, O. & Alzheimer's Disease Neuroimaging Initiative Cerebrospinal fluid analysis detects cerebral amyloid-beta accumulation earlier than positron emission tomography. Brain 139, 1226-1236 (2016).
13. Mattsson, N. et al. Predicting reduction of cerebrospinal fluid beta-amyloid 42 in cognitively healthy controls. JAMA Neurol. 72, 554-560 (2015).
14. Mattsson, N. et al. Emerging beta-amyloid pathology and accelerated cortical atrophy. JAMA Neurol. 71, 725-734 (2014).
15. Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125-1165 (2011).
16. Villemagne, V. L. et al. Longitudinal assessment of Abeta and cognition in aging and Alzheimer disease. Ann. Neurol. 69, 181-192 (2011).
17. Villeneuve, S. et al. Existing Pittsburgh Compound-B positron emission tomography thresholds are too high: statistical and pathological evaluation. Brain 138, 2020-2033 (2015).
18. Cho, H. et al. In vivo cortical spreading pattern of tau and amyloid in the Alzheimer disease spectrum. Ann. Neurol. 80, 247-258 (2016).
19. Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239-259 (1991).
20. Andrews-Hanna, J. R., Smallwood, J. & Spreng, R. N. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann. N. Y. Acad. Sci. 1316, 29-52 (2014).
21. Greicius, M. D. et al. Persistent default-mode network connectivity during light sedation. Hum. Brain Mapp. 29, 839-847 (2008).
22. Raichle, M. E. The brain's default mode network. Annu. Rev. Neurosci. 38, 433-447 (2015).
23. Buckner, R. L. et al. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J. Neurosci. 25, 7709-7717 (2005).
24. Sperling, R. A. et al. Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 63, 178-188 (2009).
25. Myers, N. et al. Within-patient correspondence of amyloid-beta and intrinsic network connectivity in Alzheimer's disease. Brain 137, 2052-2064 (2014).
26. Sepulcre, J., Sabuncu, M. R., Becker, A., Sperling, R. & Johnson, K. A. In vivo characterization of the early states of the amyloid-beta network. Brain 136, 2239-2252 (2013).
27. Cirrito, J. R. et al. Endocytosis is required for synaptic activity-dependent release of amyloid-beta in vivo. Neuron 58, 42-51 (2008).
28. Kamenetz, F. et al. APP processing and synaptic function. Neuron 37, 925-937 (2003).
29. Li, X. et al. Neuronal activity and secreted amyloid beta lead to altered amyloid beta precursor protein and presenilin 1 interactions. Neurobiol. Dis. 50, 127-134 (2013).
30. Lehmann, M. et al. Intrinsic connectivity networks in healthy subjects explain clinical variability in Alzheimer's disease. Proc. Natl Acad. Sci. USA 110, 11606-11611 (2013).
31. Buckner, R. L. et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860-1873 (2009).
32. Elman, J. A. et al. Effects of beta-amyloid on resting state functional connectivity within and between networks reflect known patterns of regional vulnerability. Cereb. Cortex 26, 695-707 (2016).
33. Koch, K. et al. Disrupted intrinsic networks link amyloid-beta pathology and impaired cognition in prodromal Alzheimer's disease. Cereb. Cortex 25, 4678-4688 (2015).
34. Bero, A. W. et al. Neuronal activity regulates the regional vulnerability to amyloid-beta deposition. Nat. Neurosci. 14, 750-756 (2011).
35. Busche, M. A. et al. Critical role of soluble amyloid-beta for early hippocampal hyperactivity in a mouse model of Alzheimer's disease. Proc. Natl Acad. Sci. USA 109, 8740-8745 (2012).
36. Augutis, K. et al. Cerebrospinal fluid biomarkers of beta-amyloid metabolism in multiple sclerosis. Mult. Scler. 19, 543-552 (2013).
37. Mattsson, N. et al. Reduced cerebrospinal fluid BACE1 activity in multiple sclerosis. Mult. Scler. 15, 448-454 (2009).
38. van Westen, D. et al. Cerebral white matter lesions - associations with Abeta isoforms and amyloid PET. Sci. Rep. 6, 20709 (2016).
39. Scholl, M. et al. Low PiB PET retention in presence of pathologic CSF biomarkers in Arctic APP mutation carriers. Neurology 79, 229-236, (2012).
40. Vandenberghe, R., Adamczuk, K., Dupont, P., Laere, K. V. & Chetelat, G. Amyloid PET in clinical practice: its place in the multidimensional space of Alzheimer's disease. Neuroimage Clin. 2, 497-511 (2013).
41. Sehlin, D. et al. Antibody-based PET imaging of amyloid beta in mouse models of Alzheimer's disease. Nat. Commun. 7, 10759 (2016).
42. Klunk, W. E. et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 11, 1-15.e4 (2015).
43. Bangen, K. J. et al. Cortical amyloid burden differences across empiricallyderived mild cognitive impairment subtypes and interaction with APOE varepsilon4 genotype. J. Alzheimers Dis. 52, 849-861 (2016).
44. Edmonds, E. C. et al. Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors. Alzheimers Dement. 11, 415-424 (2015).
45. Edmonds, E. C. et al. Subjective cognitive complaints contribute to misdiagnosis of mild cognitive impairment. J. Int. Neuropsychol. Soc. 20, 836-847 (2014).
46. Folstein, M. F., Folstein, S. E. & McHugh, P. R. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189-198 (1975).
47. Morris, J. C. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412-2414 (1993).
48. Jack, C. R. Jr. et al. The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27, 685-691 (2008).
49. Landau, S. M. et al. Measurement of longitudinal beta-amyloid change with 18F-florbetapir PET and standardized uptake value ratios. J. Nucl. Med. 56, 567-574 (2015).
50. Rasmussen, J. M. et al. Empirical derivation of the reference region for computing diagnostic sensitive (1)(8)fluorodeoxyglucose ratios in Alzheimer's disease based on the ADNI sample. Biochim. Biophys. Acta 1822, 457-466 (2012).
51. Rousset, O. G., Ma, Y. & Evans, A. C. Correction for partial volume effects in PET: principle and validation. J. Nucl. Med. 39, 904-911 (1998).
52. Han, X. & Fischl, B. Atlas renormalization for improved brain MR image segmentation across scanner platforms. IEEE Trans. Med. Imaging 26, 479-486 (2007).
53. Grabner, G., Janke, A. L., Budge, M. M., Smith, D., Pruessner, J., & Collins, D. L. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006. Proceedings, Part II. (eds Larsen, R., Nielsen, M. & Sporring J.) 58-66 (Berlin, Heidelberg, Springer, 2006).
54. Avants, B. B. et al. The Insight ToolKit image registration framework. Front. Neuroinform. 8, 44 (2014).
55. Thomas, B. A. et al. The importance of appropriate partial volume correction for PET quantification in Alzheimer's disease. Eur. J. Nucl. Med. Mol. Imaging 38, 1104-1119 (2011).
56. Olsson, A. et al. Simultaneous measurement of beta-amyloid(1-42), total tau, and phosphorylated tau (Thr181) in cerebrospinal fluid by the xMAP technology. Clin. Chem. 51, 336-345 (2005).
57. Shaw, L. M. et al. Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects. Ann. Neurol. 65, 403-413 (2009).
58. De Meyer, G. et al. Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people. Arch. Neurol. 67, 949-956 (2010).
59. Weigand, S. D. et al. Transforming cerebrospinal fluid Abeta42 measures into calculated Pittsburgh Compound B units of brain Abeta amyloid. Alzheimers Dement. 7, 133-141 (2011).
60. Palmqvist, S. et al. Accuracy of brain amyloid detection in clinical practice using cerebrospinal fluid beta-amyloid 42: a cross-validation study against amyloid positron emission tomography. JAMA Neurol. 71, 1282-1289 (2014).
61. Toledo, J. B. et al. Nonlinear association between cerebrospinal fluid and florbetapir F-18 beta-amyloid measures across the spectrum of Alzheimer disease. JAMA Neurol. 72, 571-581 (2015).
62. Palmqvist, S. et al. Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer's disease. Neurology 85, 1240-1249 (2015).
63. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M.W. & Smith, S. M. FSL. NeuroImage 62, 782-790 (2012).
64. Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162-173 (1996).
65. Avants, B. B. et al. The Insight ToolKit image registration framework. Front. Neuroinform. 8, 44 (2014).
66. Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90-101 (2007).
67. Friston, K. J., Williams, S., Howard, R. & Frackowiak, R. S. J. Movement-related effects in fMRI time-series. Magn. Reson. Med. 35, 346-55 (1996).
68. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142-2154 (2012).
69. Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. & Turner, R. Movement-related effects in fMRI time-series. Magn. Reson. Med. 35, 346-355 (1996).
70. He, H. & Liu, T. T. A geometric view of global signal confounds in resting-state functional MRI. NeuroImage 59, 2339-2348 (2012).
71. Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125-1165 (2011).
72. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968-980 (2006).
73. Zalesky, A., Fornito, A. & Bullmore, E. T. Network-based statistic: identifying differences in brain networks. NeuroImage 53, 1197-1207 (2010).
74. Benjamini, Y. & Hochberg, J. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289-300 (1995).