These findings from Cliff Jack’s group are of considerable interest for a number of different reasons. First, they confirm the importance and the strength of resting fMRI to detect gene-related (in this case ApoE) brain functional differences. Secondly, they suggest that changes in the default-mode network (DMN), previously observed during pathological aging (Greicius et al., 2004; Sorg et al., 2007), may be particularly useful in detecting individuals at risk of developing neurodegenerative disorders. Finally, paired with our previous reports in younger healthy subjects (Filippini et al., 2009), they support our recent observation that overactivity of brain function in young ApoE ε4-carriers is disproportionately reduced with advancing age even before the onset of measurable memory impairment (Filippini, 2011). Thus, the ApoE genotype seems to have different consequences for brain function...  Read more

These findings from Cliff Jack’s group are of considerable interest for a number of different reasons. First, they confirm the importance and the strength of resting fMRI to detect gene-related (in this case ApoE) brain functional differences. Secondly, they suggest that changes in the default-mode network (DMN), previously observed during pathological aging (Greicius et al., 2004; Sorg et al., 2007), may be particularly useful in detecting individuals at risk of developing neurodegenerative disorders. Finally, paired with our previous reports in younger healthy subjects (Filippini et al., 2009), they support our recent observation that overactivity of brain function in young ApoE ε4-carriers is disproportionately reduced with advancing age even before the onset of measurable memory impairment (Filippini, 2011). Thus, the ApoE genotype seems to have different consequences for brain function depending on age. These age-related changes in brain function may reflect the increased vulnerability of ApoE ε4-carriers to late-life pathology or cognitive decline. However, caution is urged. Indeed, while ApoE is the best-established genetic risk factor associated with Alzheimer’s disease, its mechanism of action has not been clearly understood yet. More research is needed to define the role played by ApoE on modulating brain function.

View all comments by Nicola Filippini

This is a very solid paper (over 50 subjects per group) that shores up our understanding of where and when ApoE4 begins to exert its influence on Alzheimer’s disease (AD). Though the sample selection and methods differ to some degree, the findings are largely in keeping with another recent study (Sheline et al., 2010) in which healthy older controls with an E4 allele showed reduced connectivity in default-mode network regions. The study by Sheline et al. examined slightly younger subjects (mean age around 60, compared to around 80 here) who had negative amyloid imaging scans. The current study does not include information on amyloid imaging, but given the advanced age of the group, we can assume that somewhere between 30 and 50 percent of the subjects would have positive amyloid imaging scans.

Both studies touch on the dorsal anterior cingulate cortex (ACC), a key node in a second network, dubbed the salience network, which appears to exist in a dynamic equilibrium with the default-mode network. In the Sheline study, the dorsal...  Read more

This is a very solid paper (over 50 subjects per group) that shores up our understanding of where and when ApoE4 begins to exert its influence on Alzheimer’s disease (AD). Though the sample selection and methods differ to some degree, the findings are largely in keeping with another recent study (Sheline et al., 2010) in which healthy older controls with an E4 allele showed reduced connectivity in default-mode network regions. The study by Sheline et al. examined slightly younger subjects (mean age around 60, compared to around 80 here) who had negative amyloid imaging scans. The current study does not include information on amyloid imaging, but given the advanced age of the group, we can assume that somewhere between 30 and 50 percent of the subjects would have positive amyloid imaging scans.

Both studies touch on the dorsal anterior cingulate cortex (ACC), a key node in a second network, dubbed the salience network, which appears to exist in a dynamic equilibrium with the default-mode network. In the Sheline study, the dorsal ACC showed more (of the expected) negative correlation to the precuneus in ApoE4 carriers. In this paper by Machulda et al., the dorsal ACC seed showed greater connectivity to several other salience network regions in the ApoE4 carriers. The Machulda paper does not report any between-network results.

In my view, the most important finding in the current study is a null result rightly highlighted by the authors. Machulda and colleagues did not find any significant differences in gray matter density between E4 carriers and non-carriers, suggesting that resting state functional connectivity may be a more sensitive measure of preclinical disease than structural MRI. Or, stated more carefully, resting-state fMRI is more sensitive to preclinical changes than is a voxelwise measure of brain structure. The authors used voxel-based morphometry and failed to find any significant differences between E4 carriers and non-carriers. They did not report other structural measures such as entorhinal or hippocampal volume which may have detected differences.

With the exception of the intriguing finding of increased medial temporal lobe and posterior cingulate connectivity in the default-mode network of very young E4 carriers (Filippini et al., 2009), all studies to date suggest that default-mode network connectivity declines in preclinical, MCI, and dementia stages of AD pathology. Resting-state functional connectivity appears to be a viable AD biomarker at the group level and may prove helpful in assessing treatment response in clinical trials. Its interpretability at the single subject level remains to be determined. The inclusion of resting-state fMRI in the second round of the Alzheimer’s disease neuroimaging initiative (ADNI2) will help the field judge the utility of this approach in assessing an individual patient.

View all comments by Michael Greicius