ApoE4 Linked to Default Network Differences in Young Adults

Whether intensely focused or thinking of nothing in particular, people carrying the ApoE4 allele show differences in brain activity at a very young age, according to a study published online 8 April in PNAS Early Edition. Researchers led by Clare MacKay at the University of Oxford, U.K., report that ApoE4 carriers have distinct patterns of activity in the “default mode network,” a group of brain areas that preferentially succumb to atrophy and amyloid deposition in Alzheimer disease. Whether the changes seen in young ApoE4 carriers relate to AD is not clear, but the new data suggest that ApoE4 does something to the brain decades before the possible onset of memory loss or neuropathology in these at-risk individuals.

The default network—a set of brain regions that perk up when the mind is adrift and tone down during periods of mental focus—has become a hotbed for brain imaging research (for a recent review, see Buckner et al., 2008). These cortical areas are among those most highly connected with other parts of the brain. In AD patients, they rack up the most amyloid (Buckner et al., 2009 and ARF related news story) and show disturbed activity (Buckner et al., 2005 and ARF related news story). Young ApoE4 carriers have abnormally low rates of glucose metabolism in some default-mode regions (Reiman et al., 2004). In the current study, first author Nicola Filipinni and colleagues complement the glucose utilization findings with functional magnetic resonance imaging (fMRI) data.

A previous investigation by Adam Fleisher, then at the University of California, San Diego, and colleagues, picked up ApoE4-linked differences in older people during task-induced fMRI (see, e.g., Fleisher et al., 2005). The new analysis extends those findings to younger adults and includes resting-state fMRI measurements, which some think may better reflect the brain’s intrinsic functional connectivity—how it is wired when not engaged in a specific task. “Inclusion of resting-state imaging is critical to understanding baseline pathophysiology and is quickly becoming utilized in the AD fMRI community,” Fleisher wrote in an e-mail to ARF. He is now the associate director of brain imaging at Banner Alzheimer’s Institute in Phoenix, Arizona.

For the new study, the researchers recruited 36 healthy people between the ages of 20 and 35. Half of the participants carried an ApoE4 allele but otherwise matched the non-carrier group in gender, age, education, and memory performance. Key differences between the two groups showed up in brain imaging data from blood oxygen level-dependent (BOLD) fMRI. In scans taken while participants were asked to recall familiar versus novel images, the ApoE4 group had higher hippocampal activation, consistent with previous studies of older ApoE4 carriers (see, e.g., Bookheimer et al., 2000 and ARF related news story; Bondi et al., 2005). Even while resting in a dark room without being asked to focus on anything, the young ApoE4 carriers in the new study showed different brain activity: they had higher activation in various parts of the default network (retrosplenial, medial temporal, and medial-prefrontal cortex regions) compared with non-carriers. Curiously, these patterns ran counter to the changes documented in patients with AD (Greicius et al., 2004) or with mild cognitive impairment (MCI) (Sorg et al., 2007), who have dampened activity in default-mode areas.

That discrepancy suggests to Bill Rebeck “that ApoE genotype has an effect on the brain independent of any effects on AD pathological changes.” Rebeck is a neuroscientist at Georgetown University Medical Center, Washington, DC, who was not involved with the new study. Considering the young age of the participants, it is hard to rationalize that the differences seen in ApoE4 carriers were due to effects on plaques and tangles, Rebeck noted in an e-mail to ARF. The new study supports a scenario in which ApoE4 may influence normal brain functions, “and then when AD pathological changes begin later in life, the two (E4 and AD) synergistically interact to drive the AD processes forward more dramatically,” he wrote.

Coupled with a recent study showing that ApoE4 gene dose tracks with amyloid load revealed by positron emission tomography (see ARF related news story), the new investigation uncovers the potential for brain imaging techniques to serve as biomarkers of disease in treatment trials for AD,” Fleisher noted. “Development of these techniques may play a key role in discovery of a cure for AD.”—Esther Landhuis

Comments

  1. This is fascinating information. I believe we are just beginning to see the potential of fMRI in the field of neuroscience. Much of my current research involves fMRI in the study of mirror neurons, and I encourage new scientists to consider further exploration of fMRI in AD research applications.

    View all comments by J. Lucy Boyd

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References

News Citations

  1. Cortical Hubs Found Capped With Amyloid
  2. Network News: Images of AD Brains Reveal Widespread Snafus
  3. ApoE(ε)4 Brains Have to Work Harder
  4. More ApoE4 Means More Amyloid in Brains of Middle-Aged

Paper Citations

  1. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008 Mar;1124:1-38. PubMed.
  2. Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, Andrews-Hanna JR, Sperling RA, Johnson KA. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci. 2009 Feb 11;29(6):1860-73. PubMed.
  3. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, Sheline YI, Klunk WE, Mathis CA, Morris JC, Mintun MA. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005 Aug 24;25(34):7709-17. PubMed.
  4. Reiman EM, Chen K, Alexander GE, Caselli RJ, Bandy D, Osborne D, Saunders AM, Hardy J. Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer's dementia. Proc Natl Acad Sci U S A. 2004 Jan 6;101(1):284-9. PubMed.
  5. Fleisher AS, Houston WS, Eyler LT, Frye S, Jenkins C, Thal LJ, Bondi MW. Identification of Alzheimer disease risk by functional magnetic resonance imaging. Arch Neurol. 2005 Dec;62(12):1881-8. PubMed.
  6. Bookheimer SY, Strojwas MH, Cohen MS, Saunders AM, Pericak-Vance MA, Mazziotta JC, Small GW. Patterns of brain activation in people at risk for Alzheimer's disease. N Engl J Med. 2000 Aug 17;343(7):450-6. PubMed.
  7. Bondi MW, Houston WS, Eyler LT, Brown GG. fMRI evidence of compensatory mechanisms in older adults at genetic risk for Alzheimer disease. Neurology. 2005 Feb 8;64(3):501-8. PubMed.
  8. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A. 2004 Mar 30;101(13):4637-42. PubMed.
  9. Sorg C, Riedl V, Mühlau M, Calhoun VD, Eichele T, Läer L, Drzezga A, Förstl H, Kurz A, Zimmer C, Wohlschläger AM. Selective changes of resting-state networks in individuals at risk for Alzheimer's disease. Proc Natl Acad Sci U S A. 2007 Nov 20;104(47):18760-5. PubMed.

Further Reading

Papers

  1. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, Sheline YI, Klunk WE, Mathis CA, Morris JC, Mintun MA. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005 Aug 24;25(34):7709-17. PubMed.
  2. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008 Mar;1124:1-38. PubMed.
  3. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A. 2004 Mar 30;101(13):4637-42. PubMed.
  4. Fleisher AS, Houston WS, Eyler LT, Frye S, Jenkins C, Thal LJ, Bondi MW. Identification of Alzheimer disease risk by functional magnetic resonance imaging. Arch Neurol. 2005 Dec;62(12):1881-8. PubMed.
  5. Sorg C, Riedl V, Mühlau M, Calhoun VD, Eichele T, Läer L, Drzezga A, Förstl H, Kurz A, Zimmer C, Wohlschläger AM. Selective changes of resting-state networks in individuals at risk for Alzheimer's disease. Proc Natl Acad Sci U S A. 2007 Nov 20;104(47):18760-5. PubMed.

Primary Papers

  1. Filippini N, Macintosh BJ, Hough MG, Goodwin GM, Frisoni GB, Smith SM, Matthews PM, Beckmann CF, Mackay CE. Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A. 2009 Apr 28;106(17):7209-14. PubMed.

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