Abstract
In this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) scans from subjects with early mild cognitive impairment (EMCI) and control subjects to study functional network connectivity. The scans were acquired by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used genetic data from the ADNI database to further subdivide the EMCI and control groups into genotype groups with or without the Apolipoprotein E allele e4 (APOE e4). Region of interest (ROI)-to-ROI resting-state functional connectivity was measured using Freesurfer and the Functional Connectivity Toolbox for Matlab (CONN). In our analysis, we compared whole-brain ROI connectivity strength and ROI-to-ROI functional network connectivity strength between EMCI, control and genotype subject groups. We found that the ROI network properties were disrupted in EMCI and APOE e4 carrier groups. Notably, we show that (1) EMCI disrupts functional connectivity strength in many important functionally-linked areas; (2) APOE e4 disrupts functional connectivity strength in similar areas to EMCI; and (3) the differences in functional connectivity between groups shows a multifactor contribution to functional network dysfunction along the trajectory leading to dementia.
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Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37, 90–101.
Buckner, R. (2004). Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron, 44, 195–208.
Buckner, R. L., & Wheeler, M. E. (2001). The cognitive neuroscience of remembering. Nature Reviews Neuroscience, 2, 624–634.
Buckner, R., Snyder, A., Shannon, B., LaRossa, G., Sachs, R., Fotenos, A., Klunk, W., & Mathis, C. (2005). Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. Journal of Neuroscience, 23(34), 7709–7717.
Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. Journal of Neuroscience 29, 1860–1873.
DeLaPaz, R. L. (1994). Echo-planar imaging. Radiographics, 14, 1045–1058.
Delbeuck, X., Collette, F., & Van der Linden, M. (2007). Is Alzheimer’s disease a disconnection syndrome? Evidence from a crossmodal audio-visual illusory experiment. Neuropsychologia, 45(14), 3315–3323.
Euston, D. R., Gruber, A. J., & McNaughton, B. L. (2002). The role of medial prefrontal cortex in memory and decision making. Neuron, 76(6), 1057–1070.
Filippini, N., MacIntosh, B., Hough, M., Goodwin, G., Frisoni, G., Smith, S., et al. (2009). Distinct patterns of brain activity in young carriers of the APOE-varepsilon4 allele. Proceedings of the National Academy of Sciences of the United States of America, 106, 7209–7214.
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102, 9673–9678.
FreeSurfer Welcome. (2014). Retrieved October 1, 2014, from http://freesurfer.net/fswiki.
Greicius, M., Srivastava, G., Reiss, A., & Menon, V. (2004). Default-mode network distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 101(13), 4637–4642.
Jessen, F., Wolfsgruber, S., Wiese, B., Bickel, H., Mosch, E., Kaduszkiewicz, H., Pentzek, M., Riedel-Heller, M. G., Luck, T., Fuchs, A., Weyerer, S., Werle, J., Van den Bussche, H., Schere, M., Maier, W., & Wagner, M. (2014). AD dementia risk in late MCI, in early MCI, and in subjective memory impairment. Alzheimer’s & Dementia, 10(1), 76–83.
Kobayashi, Y., & Amaral, D. G. (2003). Macaque monkey retro-splenial cortex: cortical afferents. Journal of Comparative Neurology, 466, 48–79.
Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nature Reviews Neuroscience, 2, 635–642.
Machulda, M. M., Jones, D. T., Vemuri, P., McDade, E., & Avula, R. (2011). Effect of APOE epsilon4 status on intrinsic network connectivity in cognitively normal elderly subjects. Archives of Neurology, 68, 1131–1136.
Mahley, R. W., Weisgraber, K. H., & Huang, Y. (2006). Apolipoprotein E4: a causative factor and therapeutic target in neuropathology, including Alzheimer’s disease. Proceedings of the National Academy of Sciences of the United States of America, 103(15), 5644–5645.
Miyashita, Y. (1993). Inferior temporal cortex: where visual perception meets memory. Annual Review of Neuroscience, 16, 245–263.
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Becket, L. (2005). Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s disease neuroimaging initiative (ADNI). Alzheimer’s Dementia, 1, 55–66.
Petersen, R., & Weiner, M. (2014). Alzheimer’s Disease Neuroimaging Initiative 2 (ADNI2) Protocol. Retrieved from www.adni-info.org/Scientists/ADNIStudyProcedure.
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Archives of Neurology, 56, 303–308.
Roid, G., Prifitera, A., & Ledbetter, M. (2007). Confirmation analysis of the factor structure of the wechsler memory scale-revised. Clinical Neuropsychologist, 2(2), 116–120.
RStudio. (2014). RStudio: Integrated development environment for R (Version 0.96.122) [Computer software]. Boston, MA. Retrieved October 20, 2014. Available from http://www.rstudio.org/
Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A., & Maris, E. (2010). Loss of ‘small-world’ networks in Alzheimer’s disease: graph analysis of FMRI resting-state functional connectivity. PloS One, 5, 13788.
Sheline, Y. I., Morris, J. C., Snyder, A. Z., Price, J. L., Yan, Z., & D’Angelo, G. (2010). APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aβ42. Journal of Neuroscience, 30(50), 17035–17040.
Sporns, O. (2011). The human connectome: a complex network. Annals of the New York Academy of Sciences, 1224, 109–125.
Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Computational Biology, 4(6), e1000100. doi:10.1371/journal.pcbi.1000100.
Tomasi, D., & Volkow, N. D. (2010). Functional connectivity density mapping. Proceedings of the National Academy of Sciences of the United States of America, 107, 9885–9890.
Trachtenberg, A. J., Filippini, N., Cheeseman, J., Duff, E. P., Neville, M. J., Ebmeier, K. P., Karpe, F., & Mackay, C. E. (2011). The effects of APOE on brain activity do not simply reflect the risk of Alzheimer's disease. Neurobiol. Aging. doi:10.1016/j.neurobiolaging.2010.11.011.
Wang, L., Zang, Y., He, Y., Liang, M., Zhang, X., Tian, L., Wu, T., Jiang, T., & Li, K. (2006). Changes in hippocampal connectivity in the early stages of Alzheimer’s disease: evidence from resting state fMRI. NeuroImage, 31, 496–504.
Wang, K., Liang, M., Wang, L., Tian, L., Zhang, X., Li, K., & Jiang, T. (2007). Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Human Brain Mapping, 28, 967–978.
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125–141. doi:10.1089/brain.2012.0073.
Zhong, Y., Huang, L., Cai, S., Zhang, Y., von Deneen, K., & Ren, A. (2014). Altered effective connectivity patterns of the default mode network in Alzheimer’s disease: an fMRI study. Neuroscience Letters, 578, 171–175.
Acknowledgments
Data collection and sharing for this project was funded by the Alzheimer’s disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare;; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Conflict of interest
Faye McKenna, Bang-Bon Koo and Ronald Killiany declare that they have no conflict of interest.
Ethical standard
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was obtained from all patients for being included in the study.
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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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McKenna, F., Koo, BB., Killiany, R. et al. Comparison of ApoE-related brain connectivity differences in early MCI and normal aging populations: an fMRI study. Brain Imaging and Behavior 10, 970–983 (2016). https://doi.org/10.1007/s11682-015-9451-z
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DOI: https://doi.org/10.1007/s11682-015-9451-z