Cited by 12Dementia and Cognitive Impairment ResearchFunctional Brain Connectivity StudiesAlzheimer's disease research and treatments

Amyloid associated intermittent network disruptions in cognitively intact older subjects: structural connectivity matters

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Mueller, Susanne G., Weiner, Michael W.·2017·Frontiers in Aging Neuroscience

Abstract

Observations in animal models suggest that amyloid can cause network hypersynchrony in the early preclinical phase of Alzheimer’s disease (AD). The aim of this study was a) to obtain evidence of paroxysmal hypersynchrony in cognitively intact subjects (CN) with increased brain amyloid load from task-free fMRI exams using a dynamic analysis approach, b) to investigate if and how hypersynchrony interferes with memory performance and c) to describe its relationship with gray and white matter connectivity. Florbetapir-F18 PET and task-free 3T functional and structural MRI were acquired in 47 CN (age =70.6±6.6), 17 were amyloid pos (florbetapir SUVR >1.11). A parcellation scheme encompassing 382 regions of interest was used to extract regional gray matter volumes, FA-weighted fiber tracts and regional BOLD signals. Graph analysis was used to characterize the gray matter atrophy profile and the white matter connectivity of each subject. The fMRI data was processed using a combination of sliding windows, graph and hierarchical cluster analysis. Each activity cluster was characterized by identifying strength dispersion (difference between pos and neg strength) their maximal and minimal pos and neg strength rois and by investigating their distribution and association with memory performance and gray and white matter connectivity using spearman rank correlations (FDR p<0.05). The cluster analysis identified 8 different activity clusters. Cluster 8 was characterized by the largest strength dispersion indicating hypersynchrony. Its duration/subject was positively correlated with amyloid load (r=0.42, p = 0.03) and negatively with memory performance (CVLT delayed recall r=-0.39 p = 0.04). The assessment of the regional strength distribution indicated a functional disconnection between mesial temporal structures and the rest of the brain. White matter connectivity was increased in left lateral and mesial temporal lobe and was positively correlated with strength dispersion in the cross-modality analysis suggesting that it enables widespread hypersynchrony. In contrast, precuneus, gray matter connectivity was decreased in the right fusiform gyrus and negatively correlated with high degrees of strength dispersion suggesting that progressing gray matter atrophy could prevent the generation of paroxysmal hypersynchrony in later stages of the disease.

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  • Callosal white matter degeneration may contribute to functional disruption of functional networks such as the default mode network.

    “our findings support the possibility that callosal white matter degeneration contribute to functional disruption of functional networks such as the default mode network (Chhatwal et al., 2013; Mueller and Weiner, 2017; Taylor et al., 2017)”

    From White matter diffusion alterations precede symptom onset in autosomal dominant Alzheimer’s disease (2018) · cites this paper as Mueller and Weiner, 2017

    The Discussion (page not provided) suggests that impaired structural connectivity could alter widespread functional connectivity as neurodegeneration progresses. It does not identify callosal white matter degeneration as the cause or establish this relationship specifically for the default mode network. (LLM, conf=0.85)

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Research works printed but not linked: 55

  1. Ayers J. I. Giasson B. I. Borchelt D. R. (2017). Prion-like spreading in tauopathies. Biol. Psychiatry. [Epub ahead of print]. 10.1016/j.biopsych.2017.04.003 28506438 PMC5640465 · DOI
  2. Bakker A. Albert M. S. Krauss G. Speck C. L. Gallagher M. (2015). Response of the medial temporal lobe network in amnestic mild cognitive impairment to therapeutic intervention assessed by fMRI and memory task performance. Neuroimage Clin. 7, 688– 698. 10.1016/j.nicl.2015.02.009 25844322 PMC4377841 · DOI
  3. Born H. A. (2015). Neuroscience forefront review: seizures in Alzheimer's disease. Neuroscience 286, 251– 263. 10.1016/j.neuroscience.2014.11.051 25484360 · DOI
  4. Braak H. Braak E. (1997). Frequency of stages of Alzheimer related-lesions in different age categories. Neurobiol. Aging 18, 351– 357. 10.1016/S0197-4580(97)00056-0 9330961 · DOI · Paper page
  5. Brier M. R. Thomas J. B. Fagan A. M. Hassenstab J. Holtzman D. M. Benzinger T. L.. (2014). Functional connectivity and graph theory in preclinical Alzheimer's disease. Neurobiol. Aging 35, 757– 768. 10.1016/j.neurobiolaging.2013.10.081 24216223 PMC3880636 · DOI
  6. Busche A. M. Konnerth A. (2016). Impairment of neuronal circuit function in Alzheimer's disease. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371: 201504429 10.1098/rstb.2015.0429 PMC4938029 27377723 · DOI · Paper page
  7. Busche M. A. Chen X. Henning H. A. Reichwald J. Staufenbiel M. Sakman B.. (2012). Critical role of soluble amyloid beta for early hippocampal hyperactivity in a mouse model of Alzheimer's disease. Proc. Natl. Acad. Sci. 109, 8740– 8745. 10.1073/pnas.1206171109 22592800 PMC3365221 · DOI · Paper page
  8. Demirtaş M. Falcon C. Tucholka A. Gispert J. D. Molinuevo J. L. Deco G. (2017). A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease. Neuroimage Clin. 16, 343– 354. 10.1016/j.nicl.2017.08.006 28861336 PMC5568172 · DOI
  9. Elman J. A. Madison C. M. Baker S. L. Vogel J. W. Mark S. M. Crowley S. (2016). Effects of beta-amyloid on resting state functional connectivity within and between networks reflects known patterns of regional vulnerability. Cereb. Cortex 26, 695– 707. 10.1093/cercor/bhu259 25405944 PMC4712800 · DOI
  10. Grienberger C. Rochefort N. L. Adelsberger H. Henning H. A. Hill D. N. Reichwald J.. (2012). Staged decline of neuronal function in vivo in an animal model of Alzheimer's disease. Nat. Commun. 3: 774 10.1038/ncomms1783 22491322 PMC3337977 · DOI
  11. Hutchison R. M. Wormelsdorf T. Allen E. A. Bandettini P. A. Calhoun V. D. Corbetta M.. (2013). Dynamic functional connectivity: promise, issues and interpretations. Neuroimage 80, 360– 378. 10.1016/j.neuroimage.2013.05.079 23707587 PMC3807588 · DOI · Paper page
  12. Irfanoglu M. O. Walker L. Sarlls J. Marceno S. Pierpaoli C. (2012). Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results. Neuroimage 61, 275– 288. 10.1016/j.neuroimage.2012.02.054 22401760 PMC3653420 · DOI
  13. Jack C. R. Wise H. J. Weigand S. D. Knopman D. S. Lowe V. Vermuri P.. (2013). Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity. Neurology 81, 1732– 1740. 10.1212/01.wnl.0000435556.21319.e4 24132377 PMC3821718 · DOI
  14. Jiang Y. Huang H. Abner E. Broster L. S. Jicha G. A. Schmitt F. A.. (2016). Alzheimer's biomarkers are correlated with brain connectivity in older adults differentially during resting and task states. Front. Aging Neurosci. 8: 15. 10.3389/fnagi.2016.00015 26903858 PMC4744860 · DOI
  15. Joliot M. Jobard G. Naveau M. Delcroix N. Petit L. Zago L.. (2015). AICHA: an atlas of intrinsic connectivity of homotopic areas. J. Neurosci. Methods 254, 46– 59. 10.1016/j.jneumeth.2015.07.013 26213217 · DOI
  16. Jones D. T. Knopman D. S. Gunter J. L. Graff-Radford J. Vermuri P. Bove B. F.. (2016). Cascading network failure across Alzheimer's disease spectrum. Brain 139, 547– 562 10.1093/brain/awv338 26586695 PMC4805086 · DOI
  17. Jones D. T. Vermuri P. Murphy M. C. Gunter J. L. Senjem M. L. Machulda M. M.. (2012). Non-stationarity in the resting brain's modular architecture. PLoS ONE 7: e39731. 10.1371/journal.pone.0039731 22761880 PMC3386248 · DOI
  18. Kang D. W. Choi W. H. Jung W. S. Um Y. H. Chang U. L. Lim H. K. (2017). Impact of amyloid burden on regional functional synchronization in the cognitively normal older adults. Sci. Rep. 7: 14690. 10.1038/s41598-017-15001-8 29089631 PMC5665874 · DOI
  19. Kantarci K. Murray M. E. Schwarz C. G. Reid R. I. Przybelski S. A. Lesnick T.. (2017). White-matter integrity on DTI and the pathologic staging of Alzheimer's disease. Neurobiol. Aging 56, 172– 179. 10.1016/j.neurobiolaging.2017.04.024 28552181 PMC5523458 · DOI · Paper page
  20. Kellner V. Menkes-Caspi N. Beker S. Stern E. A. (2014). Amyloid beta alters ongoing neuronal activity and excitability in the frontal cortex. Neurobiol. Aging 35, 1982– 1991. 10.1016/j.neurobiolaging.2014.04.001 24792906 · DOI
  21. Kleen J. K. Scott R. C. Holmes G. L. Roberts D. W. Rundle M. M. Testorf M.. (2013). Hippocampal interictal epileptiform activity disrupts cognition in humans. Neurology 81, 18– 24. 10.1212/WNL.0b013e318297ee50 23685931 PMC3770206 · DOI
  22. Kramberger M. G. Kareholt I. Andersson T. Winblad B. Eriksdotter M. Jelic V. (2013). Association between EEG Abnormalities and CSF biomarkers in a memory clinic cohort. Dement. Geriatr. Cogn. Disord. 36, 319– 328. 10.1159/000351677 24022277 · DOI
  23. Leemans A. Jones D. K. (2009). The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61, 1336– 1349. 10.1002/mrm.21890 19319973 · DOI
  24. Lewis J. Dickson D. W. (2016). Propagation of tau pathology: hypotheses, discoveries, an yet unresolved questions from experimental and human brain studies. Acta Neurobiol. 131, 27– 48. 10.1007/s00401-015-1507-z 26576562 · DOI
  25. Liedorp M. Stam C. J. van der Flier W. M. Pijnenburg Y. A. Scheltens P. (2010). Prevalence and clinical significance of epileptiform EEG discharges in a large memory clinic cohort. Dement. Geriatr. Cogn. Disord. 29, 432– 437. 10.1159/000278620 20502017 · DOI
  26. Liedorp M. van der Flier W. M. Hoogervorst E. L. J. Scheltens P. Stam C. J. (2009). Associations between patterns of EEG abnormalities and diagnosis in a large memory cohort. Dement. Geriatr. Cogn. Disord. 27, 18– 23. 10.1159/000182422 19088474 · DOI
  27. Lim H. K. Nebes R. Snitz B. Cohen A. Mathis C. Price J.. (2014). Regional amyloid burden and intrinsic connectivity networks in cognitively normal elderly subjects. Brain 137, 3327– 3338. 10.1093/brain/awu271 25266592 PMC4240287 · DOI
  28. Matura S. Prvulovic D. Butz M. Hartmann D. Sepanski B. Linnemann K.. (2014). Recognition memory is associated with altered resting-state functional connectivity in people at genetic risk for Alzheimer's disease. Eur. J. Neurosci. 40, 3128– 3135. 10.1111/ejn.12659 24989884 · DOI
  29. Mormino E. Smiljic A. Hyenga A. O. Onami S. H. Greicius M. D. Rabinovici G. D.. (2011). Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cereb. Cortex 21, 2399– 2407. 10.1093/cercor/bhr025 21383234 PMC3169663 · DOI
  30. Mucke L. Selkoe D. L. (2012). Neurotoxcicity of amyloid β protein: synaptic and network dysfunction. Cold Spring Harb. Persepct. Med. 2: a006338. 10.1101/cshperspect.a006338 22762015 PMC3385944 · DOI · Paper page
  31. Mueller S. G. Chao L. L. Berman B. Weiner M. W. (2011). Evidence for functional specialization of hippocampal subfields detected by MR subfield volumetry on high resolution images at 4T. Neuroimage 56, 851– 857. 10.1016/j.neuroimage.2011.03.028 21419225 PMC3085574 · DOI
  32. Murphy K. Birn E. M. Handwerker D. A. Jones T. B. Bandettini P. A. (2009). The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44, 893– 905. 10.1016/j.neuroimage.2008.09.036 18976716 PMC2750906 · DOI · Paper page
  33. Musiek E. S. Holtzman D. M. (2015). Three dimensions of the amyloid hypothesis: time, space and “wingman.” Nat. Neurosci. 18, 800– 806. 10.1038/nn.4018 26007213 PMC4445458 · DOI
  34. Palop J. J. Chin J. Roberson E. D. Wang J. Thwin M. T. Bien-Ly N.. (2007). Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer's disease. Neuron 55, 697– 711. 10.1016/j.neuron.2007.07.025 17785178 PMC8055171 · DOI · Paper page
  35. Palop J. J. Mucke L. (2010). Amyloid-beta-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks. Nat. Neurosci. 13, 812– 818. 10.1038/nn.2583 20581818 PMC3072750 · DOI · Paper page
  36. Palop J. J. Mucke L. (2016). Network abnormalities and interneuron dysfunction in Alzheimer disease. Nat. Rev. Neurosci. 17, 777– 792. 10.1038/nrn.2016.141 27829687 PMC8162106 · DOI · Paper page
  37. Pontecorvo M. J. Devous M. D. Navitsky M. Lu M. Sallowy S. Schaerf F. W.. (2017). Relationships between flortaucipir PET binding and amyloid burden, clinical diagnosis and cognition. Brain 140, 748– 763. 10.1093/brain/aww334 28077397 PMC5382945 · DOI · Paper page
  38. Power J. D. Barnes K. A. Snyder A. Z. Schlaggar B. L. Petersen S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142– 2154. 10.1016/j.neuroimage.2011.10.018 22019881 PMC3254728 · DOI · Paper page
  39. Quevenco F. Preti M. G. van Bergen J. M. G. Hua J. Wyss M. Li X.. (2017). Memory performance-related dynamic brain connectivity indicates pathological burden and genetic risk for Alzheimer's disease. Alzheimer Res. Ther. 9: 24. 10.1186/s13195-017-0249-7 28359293 PMC5374623 · DOI
  40. Rieckmann A. Van Dijk K. R. A. Sperling R. A. Johnson K. A. Buckner R. L. Hedden T. (2016). Accelerated decline in white matter integrity in clinically normal individuals at risk for Alzheimer's disease. Neurobiol. Aging 42, 177– 188. 10.1016/j.neurobiolaging.2016.03.016 27143434 PMC4857135 · DOI
  41. Rubinov M. Sporns O. (2011). Weight conserving characterization of complex functional brain networks. Neuroimage 56, 2068– 2079. 10.1016/j.neuroimage.2011.03.069 21459148 · DOI
  42. Schultz A. P. Chhatwal J. P. Hedden T. Mormino E. Hanseeuw B. J. Sepulcre J.. (2017). Phases of hyperconnectivity and hypoconnectivity in the default mode and salience networks track with amyloid and tau in clinically normal individuals. J. Neurosci. 37, 4323– 4331. 10.1523/JNEUROSCI.3263-16.2017 28314821 PMC5413178 · DOI · Paper page
  43. Sepulcre J. Sabuncu M. R. Li Q. El Fakhri G. Sperling R. (2017). Tau and amyloid-beta proteins distinctively associate to functional network changes in the aging brain. Alzheimers Dement. 13, 1261– 1269. 10.1016/j.jalz.2017.02.011 28366797 PMC5623176 · DOI
  44. Shah D. Praet J. Hernandez A. L. Höfling C. Anckaerts C. Bard F. (2016). Early pathogenic amyloid induces hypersynchrony of BOLD resting state networks in transgenic mice and provides an early therapeutic window before amyloid plaque deposition. Alzheimers Dement. 12, 964– 976. 10.1016/j.jalz.2016.03.010 27107518 · DOI
  45. Sheline Y. I. Raichle M. E. Snyder A. Z. Morris J. C. Head D. Wang S.. (2010). Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol. Psychiatry 67, 584– 587. 10.1016/j.biopsych.2009.08.024 19833321 PMC2829379 · DOI · Paper page
  46. Smits L. L. Liedorp M. Koene T. Roos-Reuling I. E. Lemstra A. W. Scheltens P.. (2011). EEG abnormalities are associated with different cognitive profiles in Alzheimer's disease. Dement. Geriatr. Cogn. Diord. 31, 1– 6. 10.1159/000322202 21109738 · DOI
  47. Sperling R. A. LaViolette P. S. O'Keefe K. O'Brien J. Rentz D. M. Pihlajamaki M.. (2009). Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 63, 178– 188. 10.1016/j.neuron.2009.07.003 19640477 PMC2738994 · DOI · Paper page
  48. Stargardt A. Swaab D. F. Bossers K. (2015). The storm before the quiet: neuronal hyperactivity and Abeta in the pre-symptomatic stages of Alzheimer's disease. Neurobiol. Aging 36, 1– 11. 10.1016/j.neurobiolaging.2014.08.014 25444609 · DOI · Paper page
  49. Steininger S. C. Liu X. Gietl A. Wyss M. Schreiner S. Gruber E.. (2014). Cortical amyloid beta in cognitively normal elderly adults is associated with decreased network efficiency within the cerebro-cerebellar system. Front. Aging Neurosci. 6: 52 10.3389/fnagi.2014.00052 24672483 PMC3957491 · DOI
  50. Talantova M. Sanz-Blasco S. Zhang X. Xia P. Akhtar M. W. Okamoto S. I.. (2013). Abeta induces astrocytic glutamate release, extrasynaptic NMDA receptor activation and synaptic loss. Proc. Natl. Acad. Sci. 110, 2518– 2527. 10.1073/pnas.1306832110 23776240 PMC3704025 · DOI · Paper page
  51. Tijms B. M. ten Kate M. Wink A. M. Visser P. J. Ecay M. Clerique M.. (2016). Gray matter network disruptions and amyloid beta in cognitively normal adults. Neurobiol. Aging 37, 154– 160. 10.1016/j.neurobiolaging.2015.10.015 26559882 · DOI · Paper page
  52. Vossel K. A. Beagle A. J. Rabinovici G. D. Shu H. Lee S. E. Naasan G. (2013). Seizures and epilepptiform activity in the early stages of Alzheimer's disease. JAMA Neurol. 70, 1158– 1166. 10.1001/jamaneurol.2013.136 23835471 PMC4013391 · DOI
  53. Wang L. Brier M. R. Snyder A. Z. Thomas J. B. Fagan A. M. Xiong C. (2013). Cerebrospinal fluid Abeta42, phosphorylates Tau181, and resting-state functional connectivity. JAMA Neurol. 70, 1242– 1248. 10.1001/jamaneurol.2013.3253 23959173 PMC3836828 · DOI
  54. Whitfield-Gabrieli S. Nieto-Castagnon A. (2012). Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125– 141. 10.1089/brain.2012.0073 22642651 · DOI · Paper page
  55. Wolf D. Fischer F. U. Scheurich A. Fellgiebel A. (2015). Non linear association between cerebral amyloid deposition and white matter microstructure in cognitively healthy older adults. J. Alzheimers Dis. 47, 117– 127. 10.3233/JAD-150049 26402760 · DOI · Paper page

Cited by

Sorted by citations

Authors
Mueller, S. G., & Weiner, M. W.
Affiliations
San Francisco VA Medical Center (US)
Corresponding author
Mueller, Susanne G.
Venue
Frontiers in Aging Neuroscience
Published
2017-12-19
Volume
9
Pages
418-418
ISSN
1663-4365 (ISSN-L)
OpenAlex
W2779422732
Cited by
12
References
4
Citations by year
2025: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 2
FWCI
1.12
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Aging Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • National Institutes of Health (R01 AG010897)
  • University of California, San Francisco (REAC/CTSI 37785-525205)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
amyloid, Alzheimer's disease, gray matter atrophy, white matter connectivity, medial temporal lobe, memory performance, functional disconnection, strength dispersion, graph analysis, florbetapir PET

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