In patients with AD, connectivity at baseline was decreased in the posterior default mode areas and increased in frontal regions in comparison with healthy controls. However, at follow-up, patients showed decreased add to favorites connectivity throughout the entire default mode network [13]. These results suggest that, within the default mode network, hyper-connectivity precedes hypo-connectivity of a brain region, and this may signal the early phase of brain dysfunction. Note that the observed connectivity changes follow the trajectory of neuropathology as previously described by Braak and Braak [14], which affects the medial temporal lobe first, followed by posterolateral cortical regions and, in the latest stages, the frontal cortex.

These previous findings, which outline the functional connectivity changes as the disease progresses, support the potential of resting-state fMRI as a biomarker to uncover signs of incipient AD. In our longitudinal study [13], most brain clusters that over time showed a decrease in functional connectivity in patients showed an increase in controls. Though very tentative, this finding could support the theory that this process of hyper-connectivity, which reflects functional compensation, already starts in normal aging. In their review, Vemuri and colleagues mention that, even though changes in the default mode network have been observed in normal aging, the age effect is accelerated in AD [15]. A critical question that needs to be addressed is whether we will be able to distinguish early abnormal connectivity changes from normal age-related changes.

Therefore, an avenue for future research is to study longitudinal functional connectivity changes in normal older adults and investigate the effects of certain risk factors for AD, such as genetics, gender, cognitive function, and physical fitness, on these changes. However, before resting-state functional connectivity can be implemented as a biomarker for AD, there are still some additional issues that need to be addressed. So far, the ability to meaningfully use fMRI data (that is, data of resting-state as well as task-related fMRI) on a single-subject level has been very limited. Changes in the acquisition of fMRI data could Entinostat potentially improve the signal-to-noise ratio of the data and thus increase the power of our statistical analyses.

In addition, changes in the analysis methods may further improve the sensitivity of the measurements. One example of such an improvement in the analysis methods is the creation and subsequent use of a functional brain atlas instead of a structural brain atlas for assessing functional selleck kinase inhibitor connectivity strength. Moreover, there is a lot of variability between individual patients and this possibly also explains why it has been difficult to obtain an adequate level of sensitivity and specificity by using classification procedures.