Breast cancer is one of the most frequently diagnosed cancers and the leading cause of cancer death among females, accounting for 23% of the total cancer cases [1
]. In the last decade, several neuropsychological studies have shown the negative influence of breast cancer and chemotherapy on various cognitive skills with executive function and memory impairments being the most common [2
]. These deficits have been reported both prior and following chemotherapy with evidence showing increased and/or more severe cognitive changes in breast cancer patients treated with chemotherapy [4
]. Neuroimaging studies corroborate these findings by showing changes in both brain structure and function associated with breast cancer and chemotherapy [10
]. However, it is currently unknown whether breast cancer and chemotherapy affect large-scale brain networks.
There are several lines of evidence suggesting that breast cancer may negatively impact whole brain networks. First, while the mechanisms by which breast cancer and its treatments affect cognitive function are largely unknown, possible candidates include neurotoxic effects of chemotherapy, oxidative damage, cytokine dysregulation and individual variation in genes related to neural repair and/or plasticity [2
]. These candidate mechanisms are likely to have diffuse effects on brain structure. Second, neuroimaging studies indicate that breast cancer survivors show altered brain structure, which would disrupt large-scale networks [13
]. Specifically, these patients demonstrate reduced gray matter in bilateral frontal, temporal, cerebellar, thalamic, and cingulate regions as well as decreased white matter integrity in corpus callosum, frontal, and temporal white matter tracts [14
]. Third, the specific cognitive domains that tend to be most commonly impaired in breast cancer involve executive functions and memory, as noted above. These skills are known to be subserved by distributed, integrated neural networks [18
]. Finally, cognitive impairment following breast cancer often tends to be quite subtle [2
], potentially suggesting a more diffuse brain injury. In the current study, we investigated whether breast cancer and chemotherapy are associated with alterations in large-scale structural brain networks.
Recent graph-theoretical analyses have consistently shown that brain structural networks in healthy individuals have small-world characteristics [19
]; an architecture that has dense local clustering of connections between neighboring nodes with short path length between any pair of nodes due to the existence of relatively few long-range connections [20
]. These characteristics, shared by various biological systems, reflect a network that is simultaneously highly segregated and integrated and allows for higher, more efficient rates of information processing and learning than random networks [21
Since small-world characteristics were described quantitatively for brain structural networks, there have been multiple graph-theoretical studies seeking to assess the structural correlation networks constructed from regional gray matter volume, cortical thickness and surface area [12
]. The unique feature of graph-theoretical analysis, compared with the more traditional univariate neuroimaging approaches, is that it can directly test the differences in topological parameters of the brain network such as small-worldness, highly connected hubs and regional network parameters. Whereas univariate neuroimaging approaches have typically shown limited correlations with cognitive function and dysfunction, network parameters may provide a more robust model of cognitive status [24
]. Recent graph-theoretical studies have illustrated an alteration of arrangements in structural correlation networks associated with normal aging, multiple sclerosis, Alzheimer’s disease, schizophrenia and epilepsy [26
In the present study, we applied graph theoretical analyses to compare magnetic resonance imaging (MRI)-based gray matter correlation networks of female breast cancer patients treated with chemotherapy and female healthy controls. Considering the lines of evidence regarding a diffuse pattern of gray matter atrophy in breast cancer patients, we hypothesized that such alterations should be reflected in small-world characteristics of the brain structural correlation network. We also examined the between group differences in highly connected hubs as well as in regional network measures such as node betweenness and degree.