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Structural neuroimaging studies in older adults have consistently shown volume reductions in both major and subthreshold depression. Cortical thickness, another measure of brain structure, has not been well studied in this population. We examined cortical thickness in older adults across a range of depressive symptom (DS) severity.
Forty-three community-dwelling older adults (mean age = 68.80±7.00) underwent magnetic resonance imaging. Based on a priori hypotheses, we examined cortical thickness in regions of interest (ROIs) in the rostral anterior cingulate, orbitofrontal cortex, middle frontal gyrus and isthmus cingulate using multiple linear regressions with depression questionnaire scores as the independent variable and age, sex, and mean hemispheric thickness as covariates. We also performed an exploratory whole-brain vertex-wise analysis.
After correction for multiple comparisons, we found an association between increased DSs and greater cortical thickness in the right isthmus cingulate [F(1, 38) = 8.09, FDR-corrected p = .028; R2 = 35.78] in the ROI analysis and in the left precuneus (cluster size = 413, p = 0.00002) in the vertex-wise analysis.
Older adults with higher DSs also have greater cortical thickness in the isthmus cingulate and precuneus, areas import for emotion regulation and self-referential processing. Additional research is needed to elucidate the mechanisms and potential clinical significance underlying this relationship.
Major depression is one of the most common psychiatric disorders in older adults (Seitz et al., 2010; Byers et al., 2010), with an estimated prevalence of 1–5% in individuals over age 65 years (Hasin et al., 2005). Depression can be conceptualized on a continuum, ranging from no depressive symptoms (DSs) to severe depressive disorders (Judd and Akiskal, 2000; Rodriguez et al., 2012). In older adults, subthreshold depression—symptoms of depression that do not fully meet the clinical criteria for major depression—has an estimated prevalence of 7–15% (Polyakova et al., 2014; Laborde-Lahoz et al., 2014) and is associated with adverse health outcomes (Chachamovich et al., 2008; Cuijpers et al., 2013), increased economic costs (Cuijpers et al., 2007; Meeks et al., 2011), and increased risk for major depression and other psychiatric disorders (Laborde-Lahoz et al., 2014).
Major depression in older adults has consistently been associated with structural and functional alterations in frontal-subcortical circuitry (Ajilore et al., 2014; Kumar et al., 2014b; Sexton et al., 2013; Sacher et al., 2012). The neuroanatomical basis of subthreshold symptoms has received less attention. Research has consistently shown frontal volume reductions (Kumar et al., 1998; Kumar et al., 1997b; Dotson et al., 2009b; Taki et al., 2005) in older adults as a function of DS severity, even in the subthreshold range, while fewer studies show changes in other regions, such as the temporal lobes (Dotson et al., 2009b).
The purpose of the current study was to determine whether or not DS severity is associated with cortical thickness in older adults. Cortical volume, the more common measure of brain structure in previous studies in this area, is a three-dimensional index of the brain’s structural integrity that combines cortical surface area and cortical thickness, and thus does not allow a determination of the properties that are unique to each component. There is evidence that these measurements may be driven by different underlying mechanisms (Panizzon et al., 2009; Winkler et al., 2010). Specifically, cortical thickness is thought to be more closely related to features of the cortex such as the organization of cortical layers and the size, number, and density of cell bodies in the neurons, as well as synaptic connections (Du et al., 2007; Kabani et al., 2001). Thickness and surface area appear to be genetically independent and affected by distinct neurobiological factors, such as myelin growth, during development (Panizzon et al., 2009; Rakic, 2009; White et al., 2010). Consistent with this evidence are findings of either no correlation or a weak correlation between thickness and surface area (Hogstrom et al., 2013; Winkler et al., 2010). Consequently, understanding the relationship between DSs and cortical thickness may provide additional and more specific information about the neuroanatomical basis of DSs.
In terms of major depression, most studies find cortical thinning in prefrontal, temporal, and parietal regions (Tu et al., 2012; van Eijndhoven et al., 2013; Wagner et al., 2012; Jarnum et al., 2011; Truong et al., 2013). However, some studies have reported increased cortical thickness in frontal regions, as well as the posterior cingulate, anterior insula, and temporal pole (Tu et al., 2012; Qiu et al., 2014; van Eijndhoven et al., 2013). Less work has investigated cortical thickness in older adults with subthreshold depression, although a recent study (Kumar et al., 2014a) indicated cortical thinning in the right isthmus cingulate. Based on this finding, our previous structural and functional studies of subthreshold depression (Dotson et al., 2009a; Dotson et al., 2009b), and evidence of primarily prefrontal volume deficits related to subthreshold symptoms (Ma et al., 2013; Dotson et al., 2009b; Webb et al., 2014; Kumar et al., 1998; Kumar et al., 1997a; Taki et al., 2005), we were interested in elucidating the relationship between DSs and cortical thickness in regions of interest (ROIs) in the orbitofrontal cortex, middle frontal gyrus, rostral anterior cingulate and isthmus cingulate. We also performed an exploratory whole-brain vertex-wise analysis. Given the preponderance of evidence of volumetric decreases and cortical thinning in depression, we predicted that higher DSs would be associated with cortical thinning in these regions.
Forty-nine community-dwelling older adults (mean age = 68.80±7.00) were recruited. All were right-handed, native English speakers with normal or corrected-to-normal vision, an education of at least 9 years, and a score of greater than 30, the suggested cutoff for dementia, on the Telephone Interview for Cognitive Status (TICS; Brandt et al., 1988). Initial exclusionary criteria included self-reported history of major medical or neurological illness, head trauma, learning disorders, current antiepileptic or antipsychotic use, language comprehension difficulties, and MRI contraindications. We did not exclude participants with major depression in order to increase the range of DS severity in the sample. Two participants met criteria for major depression per clinical interview. Both were taking antidepressant medication, as were five additional subjects who did not meet criteria for depression. Six subjects were excluded from the analyses due to either missing data, MRI evidence of past stroke, current substance abuse, or a learning disorder diagnosis. The local Institutional Review Board approved the study and all participants gave verbal and written informed consent. Demographic data for the final sample (n=43) are presented in Table 1.
Symptoms of depression were assessed using the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977), a 20-item self-report questionnaire measuring the frequency and severity of DSs experienced in the past week. The CES-D has been well validated for use in community-dwelling older adults (Beekman et al., 1997; Haringsma et al., 2004).
Participants received an MRI scan in a second experimental session within one week of completing the CES-D. MRI data were collected on a Phillips (Amsterdam, Netherlands) 3-Tesla scanner at the University of Florida’s McKnight Brain Institute using a Philips 8-channel radio-frequency coil. A high resolution, T1-weighted turbo field echo anatomical scan was collected using the following parameters: TR = 8.1 ms, TE = 3.7 ms, 170 slices acquired in a sagittal orientation, flip angle = 8 degrees, 1 mm cubic resolution.
Cortical reconstruction was conducted with the Freesurfer image analysis suite (version 5.3), which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). Processing included motion correction, removal of non-brain tissue (Segonne et al., 2004), automated Talairach transformation, segmentation of the subcortical structures (Fischl et al., 2004; Fischl et al., 2002), intensity inhomogeneity correction (Sled et al., 1998), tessellation of the gray/white matter boundary, topology correction (Fischl et al., 2001; Segonne et al., 2007), and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid (CSF) borders at the location where the greatest shift in intensity defines the transition to the other tissue class (Dale et al., 1999; Dale and Sereno, 1993; Fischl and Dale, 2000). This method uses both intensity and continuity information in segmentation and deformation procedures to produce representations of cortical thickness, calculated as the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the tessellated surface (Fischl and Dale, 2000). Automated cortical thickness measures have been validated against histological analysis (Rosas et al., 2002) and manual measurements (Kuperberg et al., 2003; Salat et al., 2004). Based on a priori hypotheses, cortical thickness was calculated in primary ROIs in the left and right rostral anterior cingulate, orbitofrontal cortex (calculated by summing the weighted means [regional thickness divided by regional volume] of the medial and lateral orbitofrontal cortex), middle frontal gyrus (calculated by summing the weighted means of the rostral and caudal middle frontal gyrus), and isthmus cingulate. Exploratory analyses used a whole-brain vertex-wise analysis. For each subject, a thickness measurement was mapped on each vertex of the cortical surface, allowing an examination of cortical thickness across the brain. Images were smoothed using a Gaussian kernel with a full-width-half-maximum of 10 mm.
Primary analyses were conducted using separate mixed-effects regression analyses for each ROI, performed in SAS 9.4 (Cary, NC). CES-D scores were the independent variable and age, sex, and mean total thickness were covariates. All variables besides sex were continuous measures in the models. Antidepressant use was initially included as a covariate, but was removed since it was not significant in any of the models. Statistical significance was set at a false-discovery rate (FDR; Benjamini and Hochberg, 1995) corrected threshold of α ≤ 0.05. For the exploratory whole-brain analysis, general linear models were conducted in Freesurfer with CES-D scores predicting cortical thickness at each vertex. A Monte Carlo null-Z correction for multiple comparisons was implemented to account for type I error.
Results of the primary analyses are summarized in Table 2 and Figure 1. Higher CES-D scores were significantly associated with increased thickness in the right isthmus cingulate [F(1, 38) = 8.09, p = 0.007] and the bilateral middle frontal gyrus [left F(1, 38) = 6.30, p = 0.017; right F(1, 38) = 4.25, p = 0.046]. Follow-up analyses segmenting the middle frontal gyrus found increased CES-D scores were significantly associated with increased thickness in the rostral [left F(1, 38) = 5.10, p = 0.030; right F(1, 38) = 3.79, p = 0.059], but not caudal [left F(1, 38) = 2.75, p = 0.106; right F(1, 38) = .87, p = 0.356], portion of the middle frontal gyrus. The pattern of results was unchanged when the two participants with major depression were excluded and when a covariate for antidepressant use was included. Only the effect in the right isthmus cingulate remained after FDR correction [FDR corrected p = .028]. The effect in the left middle frontal gyrus was marginal [FDR corrected p = .071].
The uncorrected vertex-wise analysis revealed multiple regions of increased thickening as a function of higher depressive symptoms, including bilateral middle frontal and inferior frontal regions (Table 3 and Figure 2). Other areas of thickening included the left precuneus and posterior cingulate, and the right middle temporal and supramarginal gyri. Only the left precuneus survived correction for multiple comparisons.
This study examined the relationship between DSs in older adults and cortical thickness using both an ROI approach centering on the cingulate and prefrontal regions, and a whole-brain analysis. We expected higher DSs to be associated with cortical thinning, but instead found significant positive relationships between DS severity and thickness in the isthmus cingulate and middle frontal gyrus in primary analysis of ROIs in cingulate and prefrontal regions. Follow-up analyses revealed an effect in the rostral, but not caudal, middle frontal gyrus. The rostral middle frontal gyrus and isthmus cingulate are portions of the dorsolateral prefrontal cortex and posterior cingulate, respectively. Both of these regions are involved in mood regulation (Beauregard et al., 2001; Price and Drevets, 2010) and have consistently been implicated in neuroimaging and post-mortem studies of major and subthreshold depression (Chang et al., 2011; Dotson et al., 2009b; Kempton et al., 2011; Liao et al., 2012; Rajkowska et al., 1999). Thus, our results are consistent with prevailing theories of the etiology of depression that focus on abnormalities in frontolimbic brain networks (e.g., Price and Drevets, 2010).
We also conducted an exploratory whole-brain analysis and found evidence of greater thickness in the left precuneus at higher DS severity. Though rarely the primary focus of neuroimaging studies of depression, this region has been implicated in multiple structural and functional studies of mood disorders (Greicius et al., 2007; Kroes et al., 2011; Li et al., 2014; Freton et al., 2014). Consistent with our findings, a recent study reported a positive relationship between scores on an anxiety/depression subscale and cortical thickness of the precuneus and posterior cingulate in young women with subthreshold depression (Ducharme et al., 2014). Moreover, there is evidence that higher DS levels are associated with larger volumes and increased activity during an emotion recognition task in the precuneus (Scheuerecker et al., 2010). The precuneus is a medial part of the parietal lobes and a core component of the default mode network. It is thought to be involved in integrating mental processing through its role in visual imagery, self-referential processing, and episodic memory (Cavanna and Trimble, 2006). In depression, the precuneus may underlie symptoms such as abnormal self-processing, over-general autobiographical memory, poor emotion regulation, and intrusive imagery involving past life stressors (Zhu et al., 2012). Our results suggest that abnormalities in the precuneus are also evident in subthreshold levels of depressive symptoms.
The present results add to a very limited literature examining cortical thickness in depression. Previous work has primarily focused on young to middle-aged samples and has yielded conflicting results. Some studies reported cortical thinning in various frontal regions, as well as the superior temporal cortex, posterior cingulate, and insula in depressed patients compared to controls (Jarnum et al., 2011; Li et al., 2014; Na et al., 2014). In older adults, two recent investigations reported that late-onset depression is associated with thinning of frontal and temporal regions and the corpus callosum compared to early-onset depression or non-depressed controls (Ballmaier et al., 2008; Lim et al., 2012). Only one known study examined subthreshold depression; older adults with minor depression were found to have cortical thinning in the right isthmus cingulate compared to controls (Kumar et al., 2014a). Of note, the right isthmus cingulate was the only region in our ROI analysis to remain significant after FDR correction, while the middle frontal gyrus became marginally significant. In contrast to thinning of the isthmus cingulate that was previously reported, we found increased thickness at higher DS levels. The duration and severity of symptoms in the two studies may explain the difference. The former study defined minor depression as having depressed mood or anhedonia plus one additional symptom for one month, and compared patients to controls. The present study used a continuous measure of scores on the CES-D, which assesses symptoms experienced in the previous week. Our results are in line with previous suggestions that cortical thickness changes differ in the early course of depressive disorders compared to later stages. For example, a recent study reported increased cortical thickness in the medial orbitofrontal, inferior frontal, rostral middle frontal, and supramarginal gyri in untreated patients with first episode mid-life depression (Qiu et al., 2014), consistent with an earlier study reporting similar increases in frontal cortical thickness in treatment-naïve depression (van Eijndhoven et al., 2013). At least three additional studies reported increased frontal thickness in major depression (Grieve et al., 2013; Reynolds et al., 2014; Tu et al., 2012), and increased thickness of the precuneus and multiple frontal regions has been associated with anxious/depressed symptoms and familial risk for depression (Ducharme et al., 2014; Peterson and Weissman, 2011).
Increased cortical thickness in first-episode treatment-naïve depressed patients led to the hypothesis that the early course of depression is associated with increased thickness, while more chronic symptoms may eventually lead to decreased thickness (Qiu et al., 2014; van Eijndhoven et al., 2013). Along those lines, some researchers argue that the early stage of depression is associated with a compensatory inflammatory response (Dowlati et al., 2010) in which astrocytes, the primary constituents of cortical tissue volume, are activated by proinflammatory cytokines and lead to cellular hypertrophy, astrocyte proliferation, process extension and interdigitation. This process in turn could increase cortical thickness (Liberto et al., 2004). Thus, increased cortical thickness may be observed in first episode depression or in the early stages of a depressive episode, while cortical thinning may result from neurotoxic effects due to prolonged physiological changes in chronic or recurrent depression (Sheline et al., 2003). Potential roles of glutamate-related toxicity and glial pathology have also been identified as important contributors to cortical thickness changes in depression (Rajkowska et al., 1999; van Eijndhoven et al., 2013). Moreover, it has been speculated that compensatory mechanisms in subthreshold depression may prevent the progression to a clinical mood disorder by mitigating negative emotional experiences associated with an overly active limbic system (Ducharme et al., 2014).
More research is needed to elucidate the mechanisms underlying increased cortical thickness in depression, and to identify demographic and clinical variables, such as age and comorbid anxiety, that may moderate the relationship between depression and cortical thickness. Information about anxiety symptoms was not available for all participants in our sample; therefore, we were not able to examine the potential impact of anxiety on the present results. The cross-sectional nature of the study is also a limitation, as we are not able to determine causality in regards to the relationship between increased thickness and DSs.
Our findings add to a limited but growing literature on cortical thickness changes in depression and provide further evidence of brain alterations as a function of DSs in older adults. Longitudinal studies are needed to elucidate causal associations. Understanding the relationship between depression and cortical thickness may increase our understanding of the pathophysiology of depression and provide new targets for treatment.
This project was funded by an Age Related memory Loss award from the McKnight Brain Research Foundation (VMD). SMS is supported by a grant from the National Institute of Aging (T32AG020499-11). AJW and AO are partially supported by the McKnight Brain Research Foundation and the University of Florida Cognitive Aging and Memory Clinical Translational Research Program. AJW is partially supported by the NIH/NCATS CTSA grant UL1 TR000064 and KL2 TR000065. VMD and SDA are partially supported by the UF Claude D. Pepper Center (NIA P30 AG028740-01). SDA is also supported by an Early Career Investigator Award from the Thomas H. Maren Foundation. The funding sources had no involvement in the study design, collection, analysis, and interpretation of the data, in the writing of the manuscript, and in the decision to submit the manuscript for publication. Neuroimaging was performed at the Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) facility in the McKnight Brain Institute of the University of Florida, which is supported by National Science Foundation Cooperative Agreement No. DMR-1157490 and the State of Florida.
The authors have no potential conflicts of interest to be disclosed.