Skip to main content

Advertisement

SpringerLink
Log in

Amyloid causes intermittent network disruptions in cognitively intact older subjects

  • Original Research
  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

Recent findings in AD models but also human patients suggest that amyloid can cause intermittent neuronal hyperactivity. The overall goal of this study was to use dynamic fMRI analysis combined with graph analysis to a) characterize the graph analytical signature of two types of intermittent hyperactivity (spike-like (spike) and hypersynchronus-like (synchron)) in simulated data and b) to attempt to identify one of these signatures in task-free fMRIs of cognitively intact subjects (CN) with or without increased brain amyloid. The toolbox simtb was used to generate 33 data sets with 2 short spike events, 33 with 2 synchron and 33 baseline data sets. A combination of sliding windows, hierarchical cluster analysis and graph analysis was used to characterize the spike and the synchron signature. Florbetapir-F18 PET and task-free 3 T fMRI was acquired in 49 CN (age = 70.7 ± 6.4). Processing the real data with the same approach as the simulated data identified phases whose graph analytical signature resembled that of the synchron signature in the simulated data. The duration of these phases was positively correlated with amyloid load (r = 0.42, p < 0.05) and negatively with memory performance (r = −0.43, p < 0.05). In conclusion, amyloid positivity is associated with intermittent hyperactivity that is caused by short phases of hypersynchronous activity. The negative association with memory performance suggests that these disturbances have the potential to interfere with cognitive processes and could lead to cognitive impairment if they become more frequent or more severe with increasing amyloid deposition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., & Eichele, T. (2014). Calhoun V (2014): Tracking whole brain connectivity dynamics in the resting state. Cerebral Cortex, 24, 663–676.

    Article  PubMed  Google Scholar 

  • An, D., Dubeau, F., & Gotman, J. (2015). BOLD responses related to focal spikes and widespread bilateral synchronous discharges generated in the frontal lobe. Epilepsia, 56, 366–374.

    Article  PubMed  Google Scholar 

  • Bai, X., Guo, J., Killory, B., Vestal, M., Berman, R., Negishi, M., Danielson, N., Novotny, E. J., Constable, R. T., & Blumenfeld, H. (2011). Resting functional connectivity between the hemispheres in childhood absence epilepsy. Neurology, 76, 1960–1967.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bakker, A., Krauss, G. L., Albert, M. S., Speck, C. L., Jones, L. R., Stark, C. R., Yassa, M. A., Bassett, S. S., Shelton, A. L., & Gallagher, M. (2012). Reduction of hippocampal hyperactivity improves cognition in amnestic mild cognitive impairment. Neuron, 74, 467–474.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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 Clinical, 7, 688–698.

    Article  PubMed  PubMed Central  Google Scholar 

  • Betzel, R., Fukushima, M., He, Y., Zuo, X.-N., & Sporns, O. (2016). Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. NeuroImage, 127, 287–297.

    Article  PubMed  Google Scholar 

  • Born, H. A. (2015). Neuroscience forefront review: Seizures in Alzheimer’s disease. Neuroscience, 286, 251–263.

    Article  CAS  PubMed  Google Scholar 

  • Brier, M. R., Thomas, J. B., Fagan, A. M., Hassenstab, J., Holtzman, D. M., Benzinger, T. L., Morris, J. C., & Ances, B. M. (2014). Functional connectivity and graph theory in preclinical Alzheimer’s disease. Neurobiology of Aging, 35, 757–768.

    Article  PubMed  Google Scholar 

  • Busche, M. A., Chen, X., Henning, H. A., Reichwald, J., Staufenbiel, M., Sakman, B., & Konnerth, A. (2012). Critical role of soluble amyloid beta for early hippocampal hyperactivity in a mouse model of Alzheimer’s disease. Proceeding of the National Academy of Science, 109, 8740–8745.

    Article  Google Scholar 

  • Carbonell, F., Bellec, P., & Shmuel, A. (2014). Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting state. NeuroImage, 86, 343–353.

    Article  CAS  PubMed  Google Scholar 

  • Chai, X., Nieto Castañon, A., & Öngür, D. (2012). Whitfield-Gabrieli S (2012): Anticorrelations in resting state networks without global signal regressions. NeuroImage, 59, 1420–1428.

    Article  PubMed  Google Scholar 

  • Chang, C., Liu, Z., Chen, M. C., Liu, X., & Duyn, J. H. (2013). EEG correlates of time-varying BOLD functional connectivity. NeuroImage, 72, 227–236.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen, J. E., Chang, C., Greicius, M. D., & Glover, G. H. (2015). Introducing co-activation pattern metrics to quantify spontaneous brain network dynamics. NeuroImage, 111, 476–488.

    Article  PubMed  PubMed Central  Google Scholar 

  • Córdova-Palomera, A., Kaufmann, T., Persson, K., Alnæs, D., Doan, N. T., Moberget, T., Lund, M. J., Barca, M. L., Engvig, A., Brækhus, A., Engedal, K., Andreassen, O. A., Selbæk, G., & Westlye, L. T. (2017). Disrupted global metastability and static and dynamic brain connectivity across individuals in the Alzheimer's disease continuum. Scientific Reports, 7, 40268.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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: Clinical, 16, 343–354.

    Article  Google Scholar 

  • De Vos F, Koini M, Schouten TM, Seiler S, van der Grond J, Lechner A, Schmidt R, van der Rooj M, Rombouts SARB (2018). A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer’s disease. NeuroImage, 167; 62–72.

  • Erhardt, E. B., Allen, E. A., Wei, Y., Eichele, T., & Calhoun, V. D. (2012). SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability. NeuroImage, 59, 4160–4167.

    Article  PubMed  Google Scholar 

  • Faizo, N. L., Burianova, H., Gray, M., Hicking, J., Galloway, G., & Reutens, R. (2014). Identification of pre-spike network in patients with temporal lobe epilepsy. Frontiers in Neurology, 5. https://doi.org/10.3389/fneur.2014.00222.

  • Grienberger, C., Rochefort, N. L., Adelsberger, H., Henning, H. A., Hill, D. N., Reichwald, J., Staufenbiel, M., & Konnerth, A. (2012). Staged decline of neuronal function in vivo in an animal model of Alzheimer’s disease. Nature Communications, 3, 774. https://doi.org/10.1038/ncomms1783.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hutchison, R. M., Wormelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., Handwerker, D. A., Keilholz, S., Kiviniemi, V., Leopold, D. A., de Pasquale, F., Sporns, O., Walter, M., & Chang, C. (2013). Dynamic functional connectivity: Promise, issues and interpretations. NeuroImage, 15, 80. https://doi.org/10.1016/j.neuroimage.2013.05.079.

  • Jack, C. R., Wise, H. J., Weigand, S. D., Knopman, D. S., Lowe, V., Vermuri, P., Mielke, M. M., Jones, D. T., Senjem, M. L., Gunter, J. L., Gregg, B. E., Pankratz, V. S., & Petersen, R. C. (2013). Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity. Neurology, 81, 1732–1740.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Joliot, M., Jobard, G., Naveau, M., Delcroix, N., Petit, L., Zago, L., Crivello, F., Mellet, E., Mazoyer, B., & Tzourio-Mazoyer, N. (2015). AICHA: An atlas of intrinsic connectivity of homotopic areas. Journal of Neuroscience Methods, 254, 46–59.

    Article  PubMed  Google Scholar 

  • Jones, D. T., Vermuri, P., Murphy, M. C., Gunter, J. L., Senjem, M. L., Machulda, M. M., Przybelski, S. A., Gregg, B. E., Kantarci, K., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Jack, C. R. (2012). Non stationarity in “resting brain’s” modular architecture. PLoS One, 7(6), e39731.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Joshi, A. D., Pontecorvo, M. J., Lu, M., Skovronsky, D. M., Mintun, M. A., & Devous, M. D. (2015). A Semiautomated method for quantification of F 18 Florbetapir PET images. The Journal of Nuclear Medicine, 56, 1736–1741.

    Article  CAS  PubMed  Google Scholar 

  • Kay, B. P., Holland, S. K., Privitera, M. D., & Szaflarski, J. P. (2014). Differences in paracingulate connectivity associated with epileptiform discharges and uncontrolled seizures in genetic generalized epilepsy. Epilepsia, 55, 256–263.

    Article  PubMed  PubMed Central  Google Scholar 

  • Khambhati, A., Mattar, M. G., Wymbs, N. F., Grafton, S. T., & Bassett, D. S. (2018). Beyond modularity: Fine scale mechanisms and rules for brain network reconfigurations. NeuroImage, 166, 385–399.

    Article  PubMed  Google Scholar 

  • Keller, C. J., Bickel, S., Honey, C. J., Groppe, D. M., Entz, L., Craddock, R. C., Lado, F. A., Kelly, C., Milham, M., & Metha, A. D. (2013). Neurophysiological investigation of spontaneous correlated and anticorrelated fluctuations of the BOLD signal. The Journal of Neuroscience, 33, 6333–6342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kellner, V., Menkes-Caspi, N., Beker, S., & Stern, E. A. (2014). Amyloid beta alters ongoing neuronal activity and excitability in the frontal cortex. Neurobiology of Aging, 35, 1982–1991.

    Article  CAS  PubMed  Google Scholar 

  • Kiviniemi, V., Vire, T., Remes, J., Elseoud, A. A., Starck, T., & Tervonen, O. (2011). Nikkinen (2011): A sliding window ICA reveals spatial variability of the default mode network in time. Brain Connectivity, 1, 339–347.

    Article  PubMed  Google Scholar 

  • Kleen, J. K., Scott, R. C., Holmes, G. L., Roberts, D. W., Rundle, M. M., Testorf, M., Lenck-Satini, P. P., & Jobst, B. C. (2013). Hippocampal interictal epileptiform activity disrupts cognition in humans. Neurology, 81, 18–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lam, A. D., Deck, G., Goldman, A., Eskandar, E. N., Noebels, J., & Cole, A. J. (2017). Silent hippocampal seizures and spikes identified by foramen ovale electrodes in Alzheimer's disease. Nature Medicine, 23, 678–680.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Leonardi, N., Richiardi, J., Gschwind, M., Simioni, S., Annoni, J. M., Schluep, M., Vuilleumier, P., & Van De Ville, D. (2013). Principal components of functional connectivity: A newapproach to study dynamic brain connectivity during rest. NeuroImage, 83, 937–950.

    Article  PubMed  Google Scholar 

  • Leonardi, N., & Van De Ville, D. (2014). On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage, 104, 430–436.

    Article  PubMed  Google Scholar 

  • 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. Dementia and Geriatric Cognitive Disorders, 27, 18–23.

    Article  CAS  PubMed  Google Scholar 

  • 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. Dementia and Geriatric Cognitive Disorders, 29, 432–437.

    Article  PubMed  Google Scholar 

  • Lopes, R., Moeller, F., Besson, P., Ogez, F., Szurhaj, W., Leclerc, X., Siniatchkin, M., Chipaux, M., Derambure, P., & Tryvaert, L. (2014). Study on the relationships between intrinsic functional connectivity of the default mode network and transient epileptic activity. Frontiers in Neurology, 5. https://doi.org/10.3389/fneur.2014.00201.

  • Liu, X., Chang, C., & Duyn, J. H. (2013). Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns. Frontiers in Systems Neuroscience Dec 4, 7, 101.

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu, J. V., Kobylarz, J., Darcey, T. M., Lu, Z., Wu, Y. C., Meng, M., & Jobst, B. C. (2014). Improved mapping of interictal epileptiform discharges with EEG-fMRI and voxel-wise functional connectivity analysis. Epilepsia, 55, 1380–1388.

    Article  PubMed  Google Scholar 

  • Mishra, A. M., Bai, X., Motelow, J. E., DeSalvo, M. N., Danielson, N., Sanganahalli, B. G., Hyder, F., & Blumenfeld, H. (2013). Increased functional connectivity in spike-wave epilepsy in WAG/RJi rats. Epilepsia, 54, 1214–1222.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mueller, S. G., & Weiner, M. W. (2017). Amyloid associated intermittent network disruptions in cognitively intact older subjects: Structural connectivity matters. Frontiers in Aging Neuroscience, 9, 418. https://doi.org/10.3389/fnagi.2017.00418.

    Article  PubMed  PubMed Central  Google Scholar 

  • Mucke, L., & Selkoe, D. L. (2012). Neurotoxcicity of amyloid beta protein: Synaptic and network dysfunction. Cold Spring Harbor Perspectives in Medicine Jul, 2(7), a006338. https://doi.org/10.1101/cshperspect.a006338.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Palop, J. J., Chin, J., Roberson, E. D., Wang, J., Thwin, M. T., Bien-Ly, N., Yoo, J., Ho, K. Q., Yu, G. Q., Kreitzer, A., FInkbeiner, S., Noebels, J. L., & Mucke, L. (2007). Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease. Neuron, 55, 697–711.

    Article  CAS  PubMed  Google Scholar 

  • Palop, J. J., & Mucke, L. (2010). Amyloid-beta-induced neuronal dysfunction in Alzheimer’s disease: From synapses toward neural networks. Nature Neuroscience, 13, 812–818.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity arise from subject motion. NeuroImage, 59, 2142–2154.

    Article  PubMed  Google Scholar 

  • Power, J. D., Mitra, A., Laumann TO, Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifacts in resting state fMRI. NeuroImage, 84, 320–341.

    Article  PubMed  Google Scholar 

  • Power, J. D., Schlaggar, B. L., & Petersen, S. E. (2015). Recent progress and outstanding issues in motion correction in resting state fMRI. NeuroImage, 105, 536–551.

    Article  PubMed  Google Scholar 

  • Preti, M. G., Bolton, T. A. W., & Van De Ville, D. (2017). The dynamic functional connectome: State of the art and perspectives. NeuroImage, 160, 41–54.

    Article  PubMed  Google Scholar 

  • Rubinov, M., Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3):1059–1069.

    Article  PubMed  Google Scholar 

  • Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional brain networks. NeuroImage, 56, 2068–2079.

    Article  PubMed  Google Scholar 

  • Sanchez, P. E., Zhu, L., Verret, L., Vossel, K. A., Orr, A. G., Cirrito, J. R., Devidze, N., Ho, K., Yu, G. Q., Palop, J. J., & Mucke, L. (2012). Levetiracetam suppresses neuronal network dysfunction and reverses synaptic and cognitive deficits in an Alzheimer disease model. Proceedings of the National Academy of Sciences, 109, 2895–2903.

    Article  Google Scholar 

  • Schultz, A. P., Chhatwal, J. P., Hedden, T., Mormino, E. C., Henseeuw, B. J., Sepulcre, J., Huijbers, W., La Point, M., Buckley, R. F., Johnson, K. A., & Sperling, R. A. (2017). Phases of hyperconnectivity and hypoconnectivity in the default mode and salience networks track with amyloid and tau in clinically normal individuals. The Journal of Neuroscience, 37, 4323–4331.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shah, D., Praet, J., Latif Hernandez, A., Höfling, C., Anckaerts, C., Bard, F., Morawski, M., Detrez, J. R., Prinsen, E., Villa, A., De Vos, W. H., Maggi, A., D'Hooge, R., Balschun, D., Rossner, S., Verhoye, M., & Van der Linden, A. (2016). Early pathologic 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.

    Article  PubMed  Google Scholar 

  • Sheline, Y. I., Raichle, M. E., Snyder, A. Z., Morris, J. C., Head, D., Wang, S., & Mintun, M. (2010). Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biological Psychiatry, 67, 584–587.

    Article  CAS  PubMed  Google Scholar 

  • Smailovic, U., Koenig, T., Kåreholt, I., Andersson, T., Kramberger, M. G., Winblad, B., & Jelic, V. (2017). Quantitative EEG power and synchronization correlate with Alzheimer's disease CSF biomarkers. Neurobiology of Aging, 63, 88–95.

    Article  CAS  PubMed  Google Scholar 

  • Smits LL, Liedorp M, Koene T, Roos-Reuling IE, Lemstra AW, Scheltens P, Stam CJ, van der Flier WM (2011): EEG abnormalities are associated with different cognitive profiles in Alzheimer’s disease. Dementia and Geriatric Cognitive Disorders 31: 1–6.

  • Sperling, R. A., LaViolette, P. S., O’Keefe, K., O’Brien, J., Rentz, D. M., Pihlajamaki, M., Marshall, G., Hyman, B. T., Selkoe, D. J., Hedden, T., Buckner, R. L., Becker, J. A., & Johnson, K. A. (2009). Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron, 63, 178–188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sporns, O., Tononi, G., & Edelman, G. M. (2000). Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex, 10, 127–141.

    Article  CAS  PubMed  Google Scholar 

  • Stargardt, A., Swaab, D. F., & Bossers, K. (2015). The storm before the quiet: Neuronal hyperactivity and Abeta in the presymptomatic stages of Alzheimer’s disease. Neurobiology of Aging, 36, 1–11.

    Article  CAS  PubMed  Google Scholar 

  • Steininger, S. C., Liu, X., Gietl, A., Wyss, M., Schreiner, S., Gruber, E., Treyer, V., Kälin, A., Leh, S., Buck, A., Nitsch, R. M., Pruessmann, K. P., Hock, C., & Unschud, P. G. (2014). Cortical amyloid beta in cognitively normal elderly adults is associated with decreased network efficiency within the cerebro-cerebellar system. Frontiers in Aging Neuroscience 18, 6, 52. https://doi.org/10.3389/fnagi.2014.0052 eCollection 2014.

  • Stern, E. A., Bacskai, B. J., Hickey, G. A., Attenello, F. J., Lombardo, J. A., & Hyman, B. T. (2004). Cortical synaptic integration in vivo is disrupted by amyloid plaques. The Journal of Neuroscience, 24, 4535–4540.

    Article  CAS  PubMed  Google Scholar 

  • Stomrud, E., Hansson, O., Minthon, L., Blennow, K., Rosén, I., & Londos, E. (2010). Slowing of EEG correlates with CSF biomarkers and reduced cognitive speed in elderly with normal cognition over 4 years. Neurobiology of Aging, 31, 215–223.

    Article  CAS  PubMed  Google Scholar 

  • Talantova, M., Sanz-Blasco, S., Zhang, X., Xia, P., Akhtar, M. W., Okamoto, S. I., Dziewczapolski, G., Nakamura, T., Cao, G., Pratt, A. E., Kang, Y. J., Tu, S., Molokanova, E., McKercher, S. R., Hires, S. A., Sason, H., Stouffer, D. G., Buczynski, M. W., Solomon, J. P., Michael, S., Powers, E. T., Kelly, J. W., Roberts, A., Tong, G., Fang-Newmeyer, T., Parker, J., Holland, E. A., Zhang, D., Nakanishi, N., Chen, H. S. V., Wolosker, H., Wang, Y., Parsons, L. H., Ambasudhan, R., Masliah, E., Heinemann, S. F., Pina-Crespo, J. C., & Lipton, S. A. (2013). Abeta induces astrocytic glutamate release, extrasynaptic NMDA receptor activation and synaptic loss. Proceedings of the National Academy of Sciences, 110, 2518–2527.

    Article  Google Scholar 

  • Vossel, K. A., Beagle, A. J., Rabinovici, G. D., Shu, H., Lee, S. E., Naasan, G., Hegde, M., Cornes, S. B., Henry, M. L., Nelson, A. B., Seeley, W. W., Geschwind, M. D., Gorno-Tempini, M. L., Shih, T., Kirsch, H. E., Garcia, P. A., Miller, B. L., & Mucke, L. (2013). Seizures and epilepptiform activity in the early stages of Alzheimer’s disease. JAMA Neurology, 70, 1158–1166.

    Article  PubMed  PubMed Central  Google Scholar 

  • Vossel, K. A., Ranasinghe, K. G., Beagle, A. J., Mizuiri, D., Honma, S. M., Dowling, A. F., Darwish, S. M., Van Berlo, V., Barnes, D. E., Mantle, M., Karydas, A. M., Coppola, G., Roberson, E. D., Miller, B. L., Garcia, P. A., Kirsch, H. E., Mucke, L., & Nagarajan, S. S. (2016). Incidence and impact of subclinical epileptiform activity in Alzheimer's disease. Annals of Neurology, 80, 858–870.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang, L., Brier, M. R., Snyder, A. Z., Thomas, J. B., Fagan, A. M., Xiong, C., Benzinger, T. L., Holtzman, D. M., Morris, J. C., & Ances, B. M. (2013). Cerebrospinal fluid Abeta42, phosphorylates Tau181, and resting-state functional connectivity. JAMA Neurology, 70, 1242–1248.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wee, C. Y., Yang, S., Yap, P. T., & Shen, D. (2016). Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging and Behavior, 10, 342–356.

    Article  PubMed  PubMed Central  Google Scholar 

  • Wennberg, R., Valiante, T., & Cheyne, D. (2011). EEG and MEG in mesial temporal lobe epilepsy. Where do the spikes really come from? Clinical Neurophysiology, 122, 1295–1313.

    Article  PubMed  Google Scholar 

  • Whitfield-Gabrieli, S., & Nieto-Castagnon, A. (2012). Conn a functional connectivity toolbox and anticorrelated brain networks. Brain Connectivity, 2, 125–141.

    Article  PubMed  Google Scholar 

  • Yang, Z., Craddock, R. C., Margulies, D. S., Yan, C. G., & Milham, M. P. (2014). Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from the analysis of temporal dynamics. NeuroImage, 93, 124–137.

    Article  PubMed  PubMed Central  Google Scholar 

  • Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014). Time resolved resting-state networks. Proceedings of the National Academy of Sciences, 111, 1034–10346.

    Article  CAS  Google Scholar 

  • Zalesky A, Breakspear M (2015): Towards a statistical test for functional connectivity dynamics. Neuroimage. https://doi.org/10.11016/j.neuroimage.2015.03.047

Download references

Acknowledgements

The study was supported by a NIH grant R01 AG010897 (PI Michael Weiner) and a UCSF grant REAC/CTSI 37785-525205 to SGM. SGM would like to thank Dr. Weiner for making his data available. Thanks also to A.M.M for the critical reading of an early draft of the manuscript.

Funding

The study was supported by NIH grant R01 AG010897 (PI Michael Weiner) and a UCSF grant REAC/CTSI 37785–525205 to SGM.

Author information

Authors and Affiliations

  1. Center for Imaging of Neurodegenerative Diseases, Department of Veterans Affairs (DVA) Medical Center, VAMC, Clement Street 4150, San Francisco, CA, 94121, USA

    Susanne G. Mueller

  2. Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA

    Susanne G. Mueller

Authors
  1. Susanne G. Mueller
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susanne G. Mueller.

Ethics declarations

Conflict of interest

The author has no conflict of interest to declare.

Ethical approval

This study was done with human participants after obtaining informed consent. All procedures performed in this study were in accordance with the ethical standards of the committees of human research at the University of California, San Francisco (UCSF) and VA Medical Center San Francisco, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mueller, S.G. Amyloid causes intermittent network disruptions in cognitively intact older subjects. Brain Imaging and Behavior 13, 699–716 (2019). https://doi.org/10.1007/s11682-018-9869-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-018-9869-1

Keywords

Search

Navigation