4.1. Relationship between hippocampal network connectivity and memory performance
After controlling for age, GDS scores and specific subject group effects, we found that positively correlated and anticorrelated hippocampal functional networks were significantly associated with memory performance and cognitive impairment. HFC strengths (m
values) have positive (positive cross-correlation value of positively correlated network) and negative (negative cross-correlation value of anticorrelated network) signs. Better memory performance (or higher RAVLT-DR scores) was associated with higher positive m
values, and stronger anticorrelation strength (more negative m
value). The hippocampus plays a prominent role in the default mode system [24
] and our results support previous findings that the DMN is organized through correlated and anticorrelated functional networks [19
]. A decrease in the correlated and anticorrelated hippocampal functional networks with poorer memory performance, as seen in this study, is consistent with the literature. Previously, disruptions in the positive and negative hippocampal functional connectivity with several frontal, parietal and DMN structures have been reported in subjects with aMCI and AD [5
]. Our results further support a distinct role of this network in memory dysfunction, among nondemented older adults.
4.2. Relationship between hippocampal network connectivity and depressive symptom severity
In this study, the greater degree of depressive symptoms was associated with increased HFC strength in the positively correlated hippocampal network in the thalamus, frontal and posterior cingulate regions after adjusting for potential confounders. We have confirmed the previous observations of persistently increased resting-state functional connectivity in individuals with depressive symptoms [30
]. A recent study in LLD showed greater caudate functional connectivity to several frontal, parietal, temporal and limbic regions [30
]. An increase in the dorsal nexus connectivity to various prefrontal and cingulate cortices was seen in depressed subjects [49
]. Another recent study found an increase in cerebral glucose metabolism in the anterior and posterior cortical structures in LLD and the elevated regional cerebral metabolism positively correlated with the depressive symptom severity [51
]. Further, stimulus-induced hyperactivity in the hippocampus and other DMN regions was previously reported in depression [50
]. Interestingly, these DMN structures show decreased activity during goal-directed behaviors in normal individuals. Our findings also support the theory that the DMN is abnormally hyperactive in older adults with depressive symptoms. The pattern of amyloid plaque deposition seen in depressed aMCI patients is similar to that found in AD [14
] and greater density of plaques and tangles within the hippocampus is found in depressed AD patients [44
]. It is plausible that the depressive symptoms-related increased hippocampal functional connectivity seen in our study may be related to the AD neuropathology.
4.3. Interactive effects of depressive symptoms and memory performance on the hippocampal functional connectivity network
Our findings of significant RAVLT-GDS interactions within the HFC networks in the frontal (medial prefrontal and SFG bilaterally), temporal (left MTG) and the posterior DMN (bilateral PCC) regions after adjusting for potential confounding variables (age and subject groups) provide important clues as to how to explore the pathophysiological mechanisms that link depression and cognitive decline. Interestingly, these brain structures where depressive symptoms and memory deficits interact are implicated in both cognitive and mood disorders in the elderly. Previously, diminished hippocampal connectivity with the DMN regions, including the frontal and posterior cingulate cortices, has been reported in older persons with aMCI and mild AD [5
], and reduced HFC-MTG connectivity is found in mild AD [5
]. In contrast, late-life depression is associated with enhanced resting-state functional connectivity and elevated cerebral glucose metabolism within the DMN and temporal lobe regions [30
]. To our knowledge, this is the first study that has demonstrated an interactive effect of memory performance and depressive symptoms on the functional neural network level.
These findings are intriguing because longitudinal studies have shown that aMCI and late-life depression separately are associated with increased future risk of developing AD [22
]. Furthermore, prevalence of comorbid depression and aMCI is common and ranges between 25% and 50% in community-based studies [1
]. Despite this, LLD studies often exclude individuals with cognitive impairment and vice versa [54
]. This is of concern because there is ongoing debate whether depressive symptoms in the elderly is a cause or a consequence of pathological cognitive decline. A rigorously conducted meta- analytic study has suggested that a history of depression independently increases the risk of developing AD by approximately twofold, supporting a causal factor hypothesis [41
]. In contrast, as the neurodegenerative changes seen in AD precede the clinical diagnosis by several years, depressive symptoms may be the earliest noncognitive manifestation of this neurodegenerative disease, supporting a reverse causality hypothesis [2
]. Moreover, elderly depressed patients who have persisting cognitive impairment after effective treatment of depression are at higher risk for conversion to dementia during follow-up [2
]. Similarly, the presence of depressive symptoms in patients with aMCI increases the future risk of developing AD relative to nondepressed aMCI subjects, although these findings are not universal [8
]. Our results suggest that these two commonly occurring behavioral phenotypes in elderly persons are competitively interactive, and this effect seems to be mediated through the hippocampal functional connections with MTG, and commonly described DMN structures.
Various pathophysiological mechanisms have been proposed to explain the link between depression and pathological cognitive decline. First, patients with coexisting geriatric depression and aMCI were found to have cortical amyloid accumulation comparable to AD, providing additional support to the theory that late-life depression may be the prodromal manifestation of AD, especially in individuals who present with coexisting aMCI [14
]. In addition, AD subjects with depression had hypoperfusion in the frontal lobe and greater neurofibrillary tangle burden in the frontal, parietal, hippocampus and other temporal cortices when compared with AD patients without depression [35
]. Therefore, the presence of depressive symptoms in aMCI may be indicative of AD neuropathological changes occurring in the DMN and temporal lobe structures, and may be suggestive of a prodromal phase of AD [8
]. Furthermore, the frontal and temporal cortices and PCC are primary sites that are affected the earliest by amyloid plaques long before the manifestation of clinical symptoms [12
]. Roughly one-quarter of cognitively healthy elderly persons show higher amyloid deposition in the frontal and temporal cortices, and the PCC/precuneus regions, which also correlate with future cognitive decline [45
]. Individuals with aMCI show greater neocortical amyloid deposition than normal subjects, and the increased amyloid plaque burden correlates with episodic memory impairment [43
]. Therefore, the existence of an interactive effect between depressive symptoms and memory deficits in these regions is likely reflective of the effects of the underlying AD neuropathology.
Second, cognitively normal older adults who develop depression are found to have an increased risk for subsequent MCI. Interestingly, a synergistic interaction between apolipoprotein E genotype and depressive symptoms was observed in that study [23
]. Finally, vascular disease that can accelerate cognitive decline in the elderly is also associated with cognitive dysfunction in geriatric depression [4
]. A recent diffusion tensor imaging study in LLD showed lower fractional anisotropy in multiple frontal and limbic regions, including the white matter adjacent to the hippocampus, PCC and select frontal and temporal regions [3
]. There are also emerging findings showing that the presence of white matter abnormalities in older adults with depression is associated with worse cognitive outcomes and subsequent development of dementia [56
]. The relationship between disruptions in the white matter integrity and altered hippocampal functional networks in the context of cognitive impairment and depression needs to be investigated.
Late-life depressive symptoms may also result in structural and functional changes within the DMN regions, and subsequently cause memory deficits. In this scenario, late-life depressive symptoms may be viewed as a risk factor for dementia [41
]. Therefore, if an interactive mechanism between depressive symptoms and memory deficits on a neural network level, as found in this study, is present, it may indicate an increased future risk of dementia in the elderly population.
The majority of the R-fMRI studies in normal and diseased states including ours averaged the time courses across the scan duration (about 6–8 min), assuming that the BOLD signal is dynamically stable over time. However, similar to task-based fMRI studies, dynamic variability in time and frequency across the course of a single scan was recently demonstrated in the DMN, suggesting the resting-state networks may not be temporally static [15
]. Interestingly, the dynamic variability that is found in the DMN is stronger after executing a task, suggesting that the temporal variance that is observed may be a residual carryover effect of the cognitive activity during the task performance [26
]. Furthermore, changes in the mood states during scanning are also shown to modulate the FC of resting-state networks in healthy subjects [29
]. Regardless, future studies should address the stability and repeatability of the resting-state FC over time scanned at different time intervals (i.e., at day or night, before or after meals or tasks).
This study is not without limitations. A comprehensive clinical diagnosis of depression was not conducted. This is a cross-sectional study and future longitudinal studies that are carefully designed are essential to examine the stability of our findings and to determine the distinct and interactive effects of memory deficits and depressive symptoms on the HFC changes over time, in older adults in whom these behavioral phenotypes coexist. Our aim in this preliminary study was to examine the distinct and interactive effects of depressive symptoms and memory deficits on the memory-associated functional brain network pattern, in a cohort of nondemented older adults. Our findings further emphasize the need to pay close attention to the coexistence of cognitive deficits and depressive symptoms and their relationships to neuronal network alterations when conducting functional imaging studies in neurodegenerative and late-life mood disorders. In addition, future functional imaging studies should assess the neurophysiological mechanisms involved in clinically diagnosed depression in the context of cognitive impairment, and the latter in the context of depression [54
]. Finally, our data analysis is limited to the HFC network, because our study was hypothesis driven. Conceivably, other neural networks may also be involved in linking memory deficits and depressive symptoms. A distributed, large-scale, whole-brain, network data-driven approach may be appropriate for this purpose.