Human aging throughout the lifespan is associated with many changes including improvements in emotion regulation 
and declines in sensory and cognitive function across a variety of domains 
. Identifying the neurobiological bases of these changes is important not only for aging research, but for cognitive neuroscience more generally, as it provides clues into the neural basis of those mental functions that are changing with age.
A common approach in aging research is to contrast young and old adults, without including a middle age cohort 
. However, those studies that have examined changes in brain function throughout the lifespan have typically reported gradual and continuous changes that begin in early adulthood and extend throughout middle age and senescence 
. Therefore, a complete understanding of the aging process requires the characterization of changes occurring in young and middle aged adults. Here we examine age-related differences in the intrinsic connectivity patterns of healthy adults in this age range.
Brain imaging studies of healthy human aging have reported changes in structure, activation, and/or connectivity patterns associated with aging in many different regions of the brain, including, but not limited to, fronto-striatal circuits 
and the so-called “default mode network” 
. More specifically, frontal cortices have been reported to have age-related decreases in volume 
and resting cerebral blood flow 
, and age-related increases in task activation 
. The default mode network has been reported to have age-related decreases in task deactivation 
, grey matter volume 
, and resting state connectivity 
. Imaging studies have also implicated many other areas of the brain in aging. For example, studies have shown decreased activation in occipital regions 
and both increased activation 
and decreased volume 
in parietal regions. The distributed and complex nature of these changes highlights the need for network level analyses of the aging human brain.
An emerging approach for studying human brain networks involves the application of graph theory 
. Studies of this nature, examining age-related changes in structural and functional brain networks, have reported decreases in whole-brain efficiency 
, decreases in cortical connectivity and local efficiency 
, and changes in the modularity structure of the brain of older adults 
. However, one of the challenges inherent in applying graph theory to functional imaging data is the definition of network nodes. A variety of approaches can be used to define network nodes, including parcellations based on functional similarity metrics 
or anatomy 
, or the definition of a specified set of regions of interest based on prior literature 
. Unfortunately, assumptions incorporated into node definition can have tremendous impacts on the resulting conclusions. For example, if a region is defined spanning functional areas with very different patterns of temporal activity, the timecourse of activity in the region will be an average of the timecourses from the functional areas comprising it. Although each of the functional areas may have strong correlations with other brain areas, those connections are unlikely to be identified when correlating to this averaged time course. To the extent that age-related brain changes involve alterations in the spatial extent of specific functional areas, this can result in different estimates of connectivity with age that are unrelated to connectivity per se. This problem tends to be more pronounced when larger regions are used as network nodes, because the regions are more likely to span functionally disparate brain areas.
Here we adopt an approach that minimizes this problem: each voxel is defined as a network node. Although computationally expensive, this approach allows unbiased exploration of the network properties of the human brain. It has revealed that the human brain has a small-world, scale-free functional architecture 
. Voxel-wise network analyses have been used successfully to identify hubs in the human brain 
, to highlight regions of the brain where network efficiency is related to cognitive function 
, to study how exercise affects brain function in older adults 
and to investigate the impact of anesthetic agents on the brain 
. In this work we apply this approach to investigate the changes in network connectivity patterns associated with healthy aging.
When applying graph theory to study human functional brain networks, the functional imaging data must first be translated into either a weighted or unweighted graph, after which one or more network properties of interest can be computed. Most prior studies have modeled functional human brain networks using unweighted graphs 
. However, this approach discards potentially valuable information regarding the strength of each connection in a graph. An alternative made possible by recent developments in weighted graph theory is to maintain information regarding the strength of each existent connection and to use that information in computing network measures. Here we explored both approaches. Using an approach similar to that shown by Buckner et al. 
, we computed the unweighted network measure of degree in a voxel-wise manner and examined the relationship between this network measure and age. In this voxel based approach the intensity of each voxel reflects the number of connections that voxel has (with correlations r>0.25) to the rest of the voxels in the gray matter. As such, a high degree measure implies that that tissue element is highly connected to the rest of the brain while a low measure of degree implies fewer connections to other brain tissue. Second, we computed the weighted graph measure of vertex strength: for each node (or vertex) in the graph, this is a summary measure of the strength of all connections to that node 
. Thus, it is an extension of the network measure of degree to a weighted graph context.
In summary, we present here an exploratory examination of age-related differences in intrinsic connectivity patterns of healthy young to middle-aged adults. Voxel-wise network measures are used, allowing an approach that is unbiased by a-priori expectations regarding regions of interest.