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Abstract

Objective

Impaired mood regulation is a key deficit of major depressive disorder that is primarily mediated by an interaction between the paralimbic cortex (i.e., orbitofrontal, cingulate, insular, parahippocampal, and temporopolar cortices) and limbic regions. The authors investigated whether depressed patients and healthy comparison subjects have differences in cortical thickness in the paralimbic cortex and whether potential differences are evident only during a depressive state or are trait related.

Method

Forty patients with a first episode of major depressive disorder participated: 20 medication-naive currently depressed patients and 20 medication-free recovered patients. The patients and 31 matched healthy comparison subjects underwent structural magnetic resonance imaging. Group differences in mean cortical thickness of the paralimbic cortex were measured by using FreeSurfer software, with adjustment for age, sex, and intracranial volume, and subgroup analyses were performed to assess state and trait effects.

Results

The medial orbitofrontal cortex was thinner in the depressed patients than in the comparison subjects. Greater thickness was present in the temporal pole and the caudal anterior and posterior cingulate cortex. All changes were trait related.

Conclusions

The data provide evidence that even early in the course of depression brain regions involved in mood regulation show trait-related differences in cortical thickness.

Major depressive disorder is a common psychiatric disorder characterized by both affective and cognitive symptoms. A core symptom of major depression that represents the depressive trait is an impairment in mood regulation (1, 2), which can be operationally defined as a vulnerability to develop a depressive state and an inability to disengage from that state (3). Investigation of structural abnormalities within the neural circuitry of mood regulation that may underlie this impairment could lead to the identification of relevant trait markers of major depression.

Mood regulation is mediated by an interaction between the paralimbic cortex (i.e., orbitofrontal, cingulate, insular, parahippocampal, and temporopolar cortices) and limbic structures, such as the amygdala (4). Based on both functional and structural aberrations in major depression, neural models of the disorder indicate that impairment in mood regulation results from failing top-down control caused by deficient corticolimbic interactions (1, 2, 5, 6). It remains unclear whether structural abnormalities of the paralimbic cortex may account for this mood regulation deficit. In particular, to our knowledge it has not been investigated whether potential structural differences of the paralimbic cortex are evident early in the course of the disease and whether they could serve as a trait marker of depression.

Histological postmortem research in major depression indicates that structural abnormalities in the paralimbic cortex may appear in the form of differences in cortical thickness, as shown by less than normal thickness within the orbitofrontal (7, 8) and anterior cingulate cortices (9, 10). However, postmortem studies leave open whether these differences in cortical thickness are a cause or a consequence of major depression, or treatment effects, and whether they reflect the acute state of depression or an underlying neurobiological trait of depression (11). Recent neuroimaging techniques, which allow the analysis of cortical thickness in vivo, might be able to answer these questions, but studies on cortical thickness in adult patients with major depression have been sparse until now.

To our knowledge, Järnum and colleagues conducted the only published study of cortical thickness within adult patients with major depression (12). They found less than normal thickness of the orbitofrontal and insular cortices in acutely depressed patients, most of whom were taking antidepressant medication (12). Unpublished data showed a thinner anterior midcingulate cortex in adults with remitted depression than in healthy subjects (M. Nagl, 2010). These results confirm that paralimbic regions are indeed structurally different in patients with major depression, but the design of the studies leaves the aforementioned questions still unanswered. Peterson and colleagues investigated unaffected persons at greater familial risk for developing depression and found large areas of cortical thinness at the lateral aspect of the right hemisphere and medial aspects of the left hemisphere (13). The widespread differences in cortical thickness seem to represent a vulnerability far beyond the domain of mood regulation, however (13), leaving the exact contribution of paralimbic regions to the depressive trait also unclear.

Results from studies in other age categories may shed light on the temporal path of cortical thickness differences over the course of the disease. Fallucca and colleagues assessed cortical thickness in children and adolescents with major depression and found some regions of greater than normal thickness and some regions with less thickness (14). Within the paralimbic cortex, the differences were limited to greater thickness of the temporal poles (14). This study compared only relatively large, predefined anatomical parcellations, however, and so it may have missed smaller regions of the paralimbic cortex showing thickness differences. Regardless, that study illustrates that cortical differences in major depression may appear in the form of greater than normal thickness and not only thinness, suggesting different neurobiological mechanisms (14). Three studies investigated cortical thickness in elderly depressed patients (1517). Only depressed elderly patients with late-onset depression, which likely represents a group with different underlying pathology, showed significant differences in cortical thickness, having thinner than normal areas in the anterior and posterior cingulate cortices and the medial orbitofrontal cortex (16).

In sum, a limited number of studies have identified differences in cortical thickness in areas of the paralimbic cortex of patients with major depression, but the results are far from consistent. This may be due to differences in methods, study group size, and patient characteristics. Further, it is also unclear whether these differences reflect trait-related abnormalities, as most studies investigated patients during the depressed state.

Therefore, we investigated cortical thickness measurements obtained with magnetic resonance imaging (MRI) in a medication-free group of 40 adult patients with major depressive disorder early in the course of depression, so that the findings would not be confounded by either neurotoxic effects of prolonged disease or neuroprotective effects of ongoing medication. The group consisted of both currently depressed patients and patients in remission, in order to allow a direct evaluation of potential state- and trait-related abnormalities in depression (18). On the basis of previous literature, we hypothesized that the patients would have regions of both greater and less than normal thickness of the paralimbic cortex, as a trait characteristic of depression.

Method

Subjects

The study participants were 40 first-episode patients with major depressive disorder and 31 matched healthy comparison subjects. Of the 40 patients with depression, 20 were medication naive and were currently experiencing their first depressive episode, and 20 were medication-free patients recovered from their first episode. The mean age of the patients with depression was 35.0 years (SD=11.5, range=18–56), and the mean age of the comparison group was 34.7 years (SD=12.0, range=18–53). Major depressive disorder was diagnosed by administration of the Structured Clinical Interview for DSM-IV (SCID) (19) by a trained psychiatrist (P.v.E.). The inclusion criterion for the currently depressed group was moderate to severe depression as defined by a score of at least 18 on the 17-item Hamilton Depression Rating Scale (20) (HAM-D). The inclusion criteria for the recovered group were the absence of clinically relevant symptoms over the preceding 6 to 24 months, defined as a score of 7 or less on the HAM-D (21), and discontinuation of antidepressant therapy for at least 2 months. Patients with other current or lifetime DSM-IV axis I disorders, as assessed with the Mini International Neuropsychiatric Interview (MINI) (22), were excluded. The inclusion criteria for the healthy comparison subjects were no lifetime DSM-IV axis I disorder, as assessed with the MINI, and no history of psychiatric disorders in first-degree relatives. All participants were otherwise healthy and did not use any medication other than hormonal contraceptives. Other exclusion criteria were a history of substance abuse or dependence, a history of traumatic brain injury, claustrophobia, metal implants, and for women, postpartum depression, pregnancy, lactation, or menopause.

The study was approved by the local ethics committee (CMO region Arnhem-Nijmegen, the Netherlands). Participants were recruited through the outpatient clinic of the department of psychiatry and by local newspaper advertisements. All participants gave written informed consent prior to participation.

Assessment

We assessed psychopathology through a clinical interview and administration of the SCID, MINI, HAM-D, Beck Depression Inventory (BDI) (23), and trait and state measures of the State-Trait Anxiety Inventory (24). Handedness was tested with the Edinburgh Handedness Inventory (25). A standard neuropsychological test battery was administered to compare neuropsychological functioning in the depressed and comparison groups: The Dutch version of the National Adult Reading Test (26) was administered as a measure of general intelligence (27). The Rey-Osterrieth Complex Figure Test (28) was used as a measure of nonverbal memory, the Dutch modified version of the Rey Auditory Verbal Learning Test (29) as a measure of immediate and delayed verbal recall and delayed recognition, the Digit Symbol Substitution Test (30) and Trail Making Test A (31) as measures of attention and psychomotor speed, and the Wisconsin Card Sorting Test (32) and the Trail Making Test interference rating as measures of executive function.

MRI Data Acquisition and Cortical Reconstruction

High-resolution anatomical images (voxel size=1 mm3) of the whole brain were acquired on a 1.5-T Siemens Sonata whole-body scanner (Siemens, Erlangen, Germany) using a three-dimensional T1-weighted magnetization prepared rapid acquisition gradient echo sequence (MPRAGE) (176 contiguous sagittal slices, volume TR=2250 ms, TE=3.93 ms, TI=850 ms, 15° flip angle, slice matrix=256×256, slice thickness=1.0 mm, no gap, field of view=256 mm) with isotropic voxels (1×1×1 mm).

The MRI data were analyzed by using FreeSurfer software (http://surfer.nmr.mgh.harvard.edu). The software package is almost completely automated and reliably computes cortical thickness. The reliability and accuracy of this method have been validated by within-subject test-retest studies, direct comparisons with manual measures on postmortem brains (33), and comparison of cross-subject regional thickness measures with published values (3436). The data were motion corrected and intensity normalized. We performed segmentation of white matter and tessellation of the gray-white matter junction. Topological defects in the gray-white estimate were fixed. Then a deformable surface algorithm was applied to find the pial surface. We visually inspected the entire cortex in each individual subject and corrected any inaccuracies in segmentation manually. The reconstructed cortical surfaces were inflated to normalize interindividual differences in gyral or sulcal depth. Each reconstructed brain was morphed and registered to an average spherical surface representation so that sulci and gyri were optimally aligned and cortical thickness difference maps could be constructed on a common spherical coordinate system.

Statistical Analysis

After smoothing (full width at half maximum, 10 mm), the cortical thickness data were averaged across participants in the spherical coordinate system, so that surface areas with significant differences of mean cortical thickness between the patients with major depressive disorder and healthy comparison subjects could be overlaid in statistical difference maps (using t statistics). First, we addressed differences between the whole group of patients and the comparison group with the general linear model functionality within QDEC, FreeSurfer’s graphical interface for analyzing group data. We estimated for the whole brain (in vertex-wise statistical difference maps) the main effect of participant group (using the FreeSurfer question “Does the average cortical thickness, accounting for sex, differ between the patients with major depression and healthy controls?” and including the nuisance factors age and total intracranial volume). On the basis of the previous literature, we a priori hypothesized that we would find both regions of greater thickness and regions of less thickness in the paralimbic cortex, as a trait characteristic of depression. We therefore report differences as significant below an uncorrected p value of 0.001 (two-tailed), which is considered an appropriate threshold when an a priori hypothesis is present (16, 37, 38). In addition, we added a correction for multiple comparisons that constrains the total number of comparisons. We created a label for the entire paralimbic cortex for each hemisphere and applied a false discovery rate (FDR) correction (p<0.05) in the FreeSurfer TK Surfer program. This label was created on the basis of the definition of paralimbic cortex by Mesulam (4) and included the following predefined cortical parcellations in FreeSurfer in the search territory: orbitofrontal, cingulate, insular, parahippocampal, and temporopolar cortices.

In addition to these, we also report all other loci beyond the a priori search territory that met the a priori statistical threshold, to allow comparison with results in other studies and to illustrate the specificity of the results (39).

Subsequently, we performed an analysis to address differences between the subgroups. In the first step, labels were created by manually aligning the differences between the total group of patients and the comparison group at p<0.001. Next, these labels were mapped back from the average to all the individual subjects in order to extract mean values of the cortical thickness for all subjects in the three subgroups (20 acutely ill patients, 20 recovered patients, and 31 healthy comparison subjects). State and trait effects on the mean cortical thickness of the regions of interest were addressed by analysis of covariance (ANCOVA), with sex, age, and total intracranial volume as covariates, among the subgroups by using SPSS software (SPSS, Chicago), and pairwise comparisons of the groups were adjusted for multiple comparisons by using Bonferroni correction. Finally, we explored the association of mean cortical thickness values for the regions of interest with scores for trait anxiety as a measure of mood regulation. We used partial correlation tests, in which we controlled for the effects of grouping by adding the BDI score as covariate.

Results

Subject Characteristics

Table 1 shows the demographic and clinical variables of the subjects, along with the results of the neuropsychological assessment. There were no significant differences between the groups on any of the demographic variables, and so potential differences in cortical thickness cannot be attributed to these measures. As expected, the patients with major depression scored higher than the healthy individuals on the self-report measures of negative mood (BDI) and state and trait anxiety. Within the group of patients, the acutely ill and recovered patients also differed on these measures and on variables related to the course of illness: HAM-D score, duration of episode, time since onset, and duration of medication use. No significant differences among the groups were observed on any of the neuropsychological tests, including measures of IQ, visual and declarative memory, attention, psychomotor speed, and executive function (see Table 1). These results show that this medication-free group of patients with first-episode depression had no apparent impairments in neuropsychological functioning.

TABLE 1. Demographic, Clinical, and Neuropsychological Characteristics of Medication-Free Acutely Ill and Recovered Patients With First-Episode Major Depressive Disorder and Healthy Comparison Subjects
Patients With Depression (N=40)
CharacteristicAcutely Ill (N=20)Recovered (N=20)Comparison Subjects (N=31)Group Effect
NNNpa
Sex0.95
 Male7612
 Female131419
Handedness0.49
 Left011
 Right201930
Treatmentb
 Previous medication018
 Cognitive-behavioral therapy (CBT)02
MeanSDMeanSDMeanSDpc
Age (years)34.111.635.811.734.712.00.90
Educational level (1–5)d3.80.93.60.63.90.80.35e
Scores on symptom measures
 Hamilton Depression Rating Scale21.84.03.42.0<0.001e
 Beck Depression Inventory27.18.89.04.72.23.0<0.001
 State-Trait Anxiety Inventory
  State43.69.036.07.232.67.6<0.001
  Trait53.312.443.79.131.16.4<0.001
Age at onset (years)33.711.433.411.50.85e
Duration of episode (months)7.15.521.614.3<0.001e
Time since onset (months)7.15.533.717.3<0.001e
Duration of medication use (months)16.711.7
IQ from National Adult Reading Test93.513.7100.18.199.017.20.29
Measures of episodic memory
 Auditory Verbal Learning Test score
  Immediate recall (number)51.010.252.88.850.37.40.74
  Delayed recall (number)10.93.111.12.311.12.20.96
 Rey-Osterrieth Complex Figure Test score22.75.521.86.123.65.10.47
Measures of attention and psychomotor speed
 Trail Making Test A (sec)30.811.126.26.727.16.30.20
 Digit Symbol Substitution Test score62.09.664.28.561.510.40.62
Measures of executive function
 Wisconsin Card Sorting Test score
  Categories completed (number)5.01.95.11.75.61.20.37
  Perseverative errors (number)14.415.516.313.210.18.70.30
 Trail Making Test interference (%)52.643.454.156.550.239.80.96

a p value of likelihood ratio.

b Treatment of the recovered patients: citalopram only, N=4; paroxetine only, N=3; fluoxetine only, N=3; CBT only, N=2; venlafaxine only, N=1; paroxetine plus fluoxetine, N=2; citalopram plus venlafaxine, N=1; paroxetine plus venlafaxine, N=1; paroxetine plus amitriptyline, N=1; citalopram, paroxetine, and venlafaxine, N=1; paroxetine, fluoxetine, and venlafaxine, N=1.

c One-way ANOVA.

d Ratings used by the Dutch education system (40); 5=postgraduate.

e One-way ANOVA with two groups only (two-sample t test).

TABLE 1. Demographic, Clinical, and Neuropsychological Characteristics of Medication-Free Acutely Ill and Recovered Patients With First-Episode Major Depressive Disorder and Healthy Comparison Subjects
Enlarge table

Cortical Thickness Analysis

In line with our hypothesis, the vertex-by-vertex analysis showed significant differences between the patients with major depression and the healthy subjects in several regions within the paralimbic cortex (p<0.001, uncorrected; adjusted for age, sex, and intracranial volume; see Figure 1 and Table 2). In the left hemisphere, the patients had significantly less thickness than the comparison subjects in the medial orbitofrontal cortex (BA 11). In addition, there were areas of greater cortical thickness in the posterior cingulate cortex (BA 23), caudal anterior cingulate cortex (BA 33), and temporal pole (BA 38). These results stayed significant when FDR correction (p<0.05) for the paralimbic cortex was applied. In the right hemisphere, the only significant difference was greater cortical thickness in the temporal pole (BA 38) of the patients with depression, but this did not remain significant after FDR correction. There were no areas of significantly less thickness in the right hemisphere. Post hoc inspection of the entire brain showed that only one area outside the a priori search territory met the statistical threshold, the right rostral middle frontal cortex (BA 10; x=32.0, y=56.4, z=−9.4; p=0.0005), which indeed illustrates the relative specificity of findings within the paralimbic cortex (39).

FIGURE 1. Areas of the Cerebral Cortex Showing Less or More Thickness in Patients With First-Episode Major Depressive Disorder (N=40) Than in Healthy Comparison Subjects (N=31)a

a The patients with major depression had less thickness than the comparison subjects in the left medial orbitofrontal cortex and greater thickness of the bilateral temporal poles, left posterior cingulate cortex, and left rostral anterior cingulate cortex. On the color scale, negative p values indicate less thickness in the patients and positive values indicate greater thickness.

TABLE 2. Brain Regions With Significant Differences in Cortical Thickness Between Patients With First-Episode Major Depressive Disorder and Healthy Comparison Subjects
Coordinates (mm)a
Cortical Thickness (mm)
Patients With Depression (N=40)
Comparison Subjects (N=31)
Cortical RegionxyzMeanSDMeanSDEffect Size (Cohen’s d)ANCOVA (p)b
Thinner in patients
 Left medial orbitofrontal cortex–6.029.2–19.02.560.182.840.37–0.96<0.0001
Thicker in patients
 Left posterior cingulate cortex–5.2–27.130.72.550.482.180.440.80<0.0001
 Left caudal anterior cingulate cortex–5.931.614.73.530.473.140.440.89<0.001
 Left temporal pole–31.410.6–34.63.820.523.310.620.88<0.001
 Right temporal pole28.88.5–38.13.700.343.280.421.11<0.001

a Based on the Talairach and Tournoux system (41).

b With the covariates gender, age, and intracranial volume.

TABLE 2. Brain Regions With Significant Differences in Cortical Thickness Between Patients With First-Episode Major Depressive Disorder and Healthy Comparison Subjects
Enlarge table

Regions with significant differences between the groups were subsequently defined as regions of interest. Five regions of interest (left medial orbitofrontal cortex, left posterior cingulate cortex, left caudal anterior cingulate, and bilateral temporal pole) were used for subgroup analyses (ANCOVA) and correlations with clinical variables.

To further explore our hypothesis that differences in cortical thickness are due to trait effects and not to state effects, we used the mean cortical thickness of the regions of interest to perform a secondary analysis to test differences between the subgroups of acutely ill patients (N=20) and recovered patients (N=20) and between each patient subgroup and the healthy comparison subjects (N=31) (see Table 3 and visual presentation in Figure 2). These analyses showed that mean cortical thickness for the regions of interest differed from the healthy comparison subjects in both the acutely ill and recovered patients, although the difference between the acutely ill patients and comparison group for the left temporal pole fell short of significance. There were no significant differences between the acutely ill and recovered patients, confirming the trait character of the differences in cortical thickness.

TABLE 3. Comparison of Cortical Thickness in Regions of Interest in Acutely Ill and Recovered Patients With Major Depressive Disorder and in Healthy Comparison Subjects
Cortical Thickness (mm)
Patients With Depression
Acutely Ill (N=20)
Recovered (N=20)
Comparison Subjects (N=31)
Group Effect (df=2, 65)a
Pairwise Comparisons (p)b
Region of InterestMeanSDMeanSDMeanSDFpAcutely Ill Versus ComparisonRecovered Versus ComparisonAcutely Ill Versus Recovered
Left medial orbitofrontal cortex2.480.202.640.132.840.3711.71<0.001<0.0010.030.24
Left posterior cingulate cortex2.490.392.610.572.180.447.540.0010.030.0021.00
Left caudal anterior cingulate cortex3.520.573.550.373.140.445.130.009<0.050.031.00
Left temporal pole3.700.523.930.503.310.626.180.0040.070.0051.00
Right temporal pole3.710.303.690.383.280.428.450.0010.0020.0071.00

a ANCOVA of the three groups with the covariates gender, age, and intracranial volume.

b With Bonferroni adjustment for multiple comparisons.

TABLE 3. Comparison of Cortical Thickness in Regions of Interest in Acutely Ill and Recovered Patients With Major Depressive Disorder and in Healthy Comparison Subjects
Enlarge table
FIGURE 2. Cortical Thickness in Regions of Interest in Acutely Ill (N=20) and Recovered (N=20) Patients With First-Episode Major Depressive Disorder and in Healthy Comparison Subjects (N=31)a

a Horizontal lines indicate mean values.

Correlation With Mood Regulation

Given our hypothesis that differences in cortical thickness of the paralimbic cortex would be related to differences in mood regulation, we correlated scores for trait anxiety, as a measure of mood regulation, with cortical thickness in the aforementioned regions of interest. As this relation could mediate a vulnerability effect that may also be evident in healthy subjects, we computed the partial correlations across all subjects, controlling for BDI score to account for the effects of grouping. In the total group of subjects, the thickness of the left medial orbitofrontal cortex was inversely correlated with the score for trait anxiety when the BDI score was controlled for (r=−0.48, N=71, p<0.001).

To further explore possible state and trait effects of this correlation, we analyzed correlations between the thickness of the left medial orbitofrontal cortex and the BDI-controlled trait anxiety score within the separate subgroups (acutely ill, recovered, and comparison). There was no significant correlation with trait anxiety in either depression subgroup, but in the group of healthy subjects the thickness of the left orbitofrontal cortex correlated inversely with trait anxiety (r=−0.59, N=31, p=0.003; see Figure 3). There were no significant correlations between trait anxiety and the cortical thickness of the other regions of interest.

FIGURE 3. Correlation Between Thickness of the Left Medial Orbitofrontal Cortex and Trait Anxiety in Healthy Comparison Subjects

Discussion

In this study we found trait-related differences in the thickness of the paralimbic cortex between medication-free first-episode patients with major depressive disorder and healthy comparison subjects. Prominent thinness was found within the medial orbitofrontal cortex of the left hemisphere in patients with major depressive disorder. An interesting finding was a correlation between the thickness of this region and a clinical variable indicating depression vulnerability in the comparison group. The patients had greater thickness than the comparison subjects in the bilateral temporal poles, left posterior cingulate cortex, and left rostral anterior cingulate cortex. Patterns of thickness in the mood-regulating paralimbic cortex (4) may therefore reveal key pathophysiological mechanisms of major depressive disorder.

Areas of Less Cortical Thickness

Our results revealed a trait-related thinness of the left medial orbitofrontal cortex. A large body of evidence supports involvement of the medial orbitofrontal cortex in mood disorders (4244), implicating both lower volume and functional abnormalities in this region, with a tendency toward the left hemisphere (for a review see reference 45). Indeed, thinness in the left medial orbitofrontal cortex was recently shown in multiepisode patients with major depression (12) and elderly patients with late-onset depression (16). Our data indicate that this regional thinness, rather than being the result of prolonged disease, is already present in the early course of depression. This finding has several implications.

First, our data suggest that the observed thinness constitutes a trait factor in depression, as differences were present in both the acutely depressed and recovered subgroups of patients with major depression. These conclusions are supported by a finding of thinness of the left medial orbitofrontal cortex in individuals at risk for familial depression (13). Second, our correlational analysis across both patients and comparison subjects revealed that a higher score for trait anxiety, indicating impaired mood regulation, is associated with a thinner cortex in this specific area. Although we controlled for BDI score, grouping effects may influence this correlation in the total study group. The correlation with trait anxiety seems to indicate a genuine vulnerability aspect, however, as the thickness of this area in the healthy subjects was inversely correlated with trait anxiety, replicating a recent finding by Kühn and colleagues (46). High trait anxiety is considered a risk factor for major depression (47, 48), corresponding to neuroticism, that is inversely correlated with thickness of the medial orbitofrontal cortex (49). Therefore, thinness of the medial orbitofrontal cortex could reflect a cause rather than a consequence of major depression.

Fundamental, postmortem research revealed that a thinner medial orbitofrontal cortex in major depression is caused by cytoarchitectural abnormalities involving neurons and glial cells (7, 8). It is likely that the vulnerability for depression is mediated by lower inhibitory control over limbic structures, such as the amygdala (45). Less thickness leads to a lower capacity to limit emotional responses and autonomic reactions (50). Our results did not show a significant difference in thickness between the acutely ill and recovered patients, although it remains possible that thinness may be partly reversible and might respond to neurotrophic effects of antidepressant medication. One study found an increase of orbitofrontal thickness in patients with major depression at follow-up (12). Further study is certainly needed to investigate the effect of treatment on thickness of the medial orbitofrontal cortex.

Areas of Greater Cortical Thickness

Our study also revealed trait-related greater thickness of the bilateral temporal poles, left rostral anterior cingulate, and posterior cingulate cortex. Greater thickness of the temporal poles was recently reported in children with major depression (14). The temporal pole couples emotional responses to highly processed sensory information, and this integration is critical for the evaluation of emotional states (51, 52). Damage of the temporal pole can lead to unstable mood states (53, 54), and previous neuroimaging studies in major depression have detected both lower than normal gray matter density (55) and high activation (56) within this region.

The rostral anterior cingulate cortex and the posterior cingulate cortex are involved in several aspects of emotion regulation, such as appraisal and expression of negative emotion (57), voluntary suppression of negative affect (6, 58), and self-referential functions (59). Low volume and greater than normal activation in major depression have been consistently shown for both regions (for an overview see reference 5). A recent coordinate-based meta-analysis of 23 voxel-based morphometry studies in major depression identified one single, bilateral cluster of low gray matter volume in the rostral anterior cingulate cortex (60), in the exact location where our study detected greater than normal thickness. Of note is that this lower volume was not observed in study groups that included only first-episode patients. Hence, our findings do not per se contradict later thinning as a function of progressive neurotoxic effects on the anterior cingulate cortex (60).

It is still unclear what this greater cortical thickness means. One speculative explanation could be that these regions are recruited to a greater degree because of dysfunction of the orbitofrontal cortex. Deficient inhibitory control over limbic structures leads to the activation of other regions involved in mood regulation (5, 6). Greater cortical thickness in paralimbic regions may reflect a compensatory mechanism to cope with less efficient self-regulation in major depression. Such a mechanism is consistent with greater posterior cingulate thickness in patients with remitted major depression than in patients without remission (12). Increased thickness due to compensatory activity could well reflect an altered developmental trajectory during childhood or adolescence, in line with the work of Fallucca and colleagues (14). Alternatively, the greater thickness may be the first evidence of an underlying pathological process, which eventually leads to volumetric reduction, as evidenced by the correspondence of the locations of greater thickness in our study and progressive gray matter reduction in the rostral anterior cingulate cortex in the recent meta-analysis (60).This process may be related to glutamate-related toxicity, which is known to affect glial function (61). According to Rajkowska and colleagues, the early stages of major depressive disorder are characterized by glial pathology, which includes hypertrophy of glial cells through compensatory mechanisms. These compensatory mechanisms are unable to prevent the accumulation of extracellular glutamate in the longer run, however, leading to damage and loss of neurons in the advanced stages of major depression (11).

We can only speculate about cellular changes underlying cortical thickening, such as increased gliagenesis or neurogenesis. Emerging evidence indicates that neurogenesis can also occur in the adult neocortex of mammals (6264). Whatever the causes, greater thickness of these paralimbic areas may induce overrecruitment of this circuitry through kindling mechanisms, which could lead to heightened sensitivity to emotional triggers. Of note is that the areas of greater thickness observed in our study are all part of the default mode system (5). Previous research has shown that depressed patients have particular problems with deactivation of the default mode system (65), and there is evidence for greater than normal functional connectivity within the default mode network, both during acute episodes and after recovery (66). Possibly, greater functional connectivity between regions implicated in the default mode network and the lack of deactivation of these regions may be related to greater cortical thickness in these particular areas, although the causal relationship remains to be elucidated.

Limitations

The limitations of this study include the use of a cross-sectional design and potential indirect medication effects. Although all participants were currently free of medication, the recovered patients had received pharmacotherapy prior to investigation. Longitudinal studies are required to conclusively identify trait effects and investigate the temporal course of thickness in the paralimbic cortex. It would be informative to include patients with familial depression and their unaffected offspring to disentangle environmental and genetic effects and to determine whether trait effects are already present premorbidly. Future studies would benefit from higher-resolution imaging and could be combined with magnetic resonance spectroscopy and resting-state functional MRI to relate cortical thickness to levels of glutamate and default mode network activity.

In sum, our data show that the depressive trait is associated, on a group level, with differences in the thickness of the paralimbic cortex. In particular, thinness of the medial orbitofrontal cortex may serve as a potential neurobiological endophenotype of major depressive disorder, behaviorally corresponding to impaired mood regulation. Although our measurements are from fairly homogeneous medication-free groups, the results are limited by the study’s cross-sectional nature and should be replicated in a study with a longitudinal design.

From the Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition, and Behavior, Nijmegen, the Netherlands; GGz Centraal, Amersfoort, the Netherlands; the Department of Psychiatry, University of Cologne, Cologne, Germany; the Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, the Netherlands; and the Department of Psychiatry, University of Duisburg-Essen, Essen, Germany.
Address correspondence to Dr. van Eijndhoven ().

In the past 3 years Dr. Buitelaar has been a consultant to, member of an advisory board of, and/or speaker for Janssen Cilag, Eli Lilly and Sons, Shire, Novartis, Roche, and Servier; he is not an employee of any of these companies and not a stock shareholder of any of these companies; he has no other financial or material support, including expert testimony, patents, or royalties. The other authors report no financial relationships with commercial interests.

References

1 Phillips ML, Drevets WC, Rauch SL, Lane R: Neurobiology of emotion perception II: implications for major psychiatric disorders. Biol Psychiatry 2003; 54:515–528Crossref, MedlineGoogle Scholar

2 Mayberg HS: Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment. Br Med Bull 2003; 65:193–207Crossref, MedlineGoogle Scholar

3 Holtzheimer PE, Mayberg HS: Stuck in a rut: rethinking depression and its treatment. Trends Neurosci 2011; 34:1–9Crossref, MedlineGoogle Scholar

4 Mesulam MM: From sensation to cognition. Brain 1998; 121:1013–1052Crossref, MedlineGoogle Scholar

5 Drevets WC, Price JL, Furey ML: Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression. Brain Struct Funct 2008; 213:93–118Crossref, MedlineGoogle Scholar

6 Phillips ML, Ladouceur CD, Drevets WC: A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Mol Psychiatry 2008; 13:829, 833–857Crossref, MedlineGoogle Scholar

7 Rajkowska G, Miguel-Hidalgo JJ, Wei J, Dilley G, Pittman SD, Meltzer HY, Overholser JC, Roth BL, Stockmeier CA: Morphometric evidence for neuronal and glial prefrontal cell pathology in major depression. Biol Psychiatry 1999; 45:1085–1098Crossref, MedlineGoogle Scholar

8 Rajkowska G, Miguel-Hidalgo JJ, Dubey P, Stockmeier CA, Krishnan KR: Prominent reduction in pyramidal neurons density in the orbitofrontal cortex of elderly depressed patients. Biol Psychiatry 2005; 58:297–306Crossref, MedlineGoogle Scholar

9 Cotter D, Mackay D, Landau S, Kerwin R, Everall I: Reduced glial cell density and neuronal size in the anterior cingulate cortex in major depressive disorder. Arch Gen Psychiatry 2001; 58:545–553Crossref, MedlineGoogle Scholar

10 Ongür D, Drevets WC, Price JL: Glial reduction in the subgenual prefrontal cortex in mood disorders. Proc Natl Acad Sci USA 1998; 95:13290–13295Crossref, MedlineGoogle Scholar

11 Rajkowska G, Miguel-Hidalgo JJ: Gliogenesis and glial pathology in depression. CNS Neurol Disord Drug Targets 2007; 6:219–233Crossref, MedlineGoogle Scholar

12 Järnum H, Eskildsen SF, Steffensen EG, Lundbye-Christensen S, Simonsen CW, Thomsen IS, Fründ ET, Théberge J, Larsson EM: Longitudinal MRI study of cortical thickness, perfusion, and metabolite levels in major depressive disorder. Acta Psychiatr Scand 2011; 124:435–446Crossref, MedlineGoogle Scholar

13 Peterson BS, Warner V, Bansal R, Zhu H, Hao X, Liu J, Durkin K, Adams PB, Wickramaratne P, Weissman MM: Cortical thinning in persons at increased familial risk for major depression. Proc Natl Acad Sci USA 2009; 106:6273–6278Crossref, MedlineGoogle Scholar

14 Fallucca E, MacMaster FP, Haddad J, Easter P, Dick R, May G, Stanley JA, Rix C, Rosenberg DR: Distinguishing between major depressive disorder and obsessive-compulsive disorder in children by measuring regional cortical thickness. Arch Gen Psychiatry 2011; 68:527–533Crossref, MedlineGoogle Scholar

15 Koolschijn PC, van Haren NE, Schnack HG, Janssen J, Hulshoff Pol HE, Kahn RS: Cortical thickness and voxel-based morphometry in depressed elderly. Eur Neuropsychopharmacol 2010; 20:398–404Crossref, MedlineGoogle Scholar

16 Ahn KJ, Won WY, Hahn C, Lee SY, Kim I, Lee CU: Regional cortical thickness and subcortical volume changes are associated with cognitive impairments in the drug-naive patients with late-onset depression. Neuropsychopharmacology 2012; 37:838–849Crossref, MedlineGoogle Scholar

17 Colloby SJ, Firbank MJ, Vasudev A, Parry SW, Thomas AJ, O’Brien JT: Cortical thickness and VBM-DARTEL in late-life depression. J Affect Disord 2011; 133:158–164Crossref, MedlineGoogle Scholar

18 van Eijndhoven P, van Wingen G, van Oijen K, Rijpkema M, Goraj B, Jan Verkes R, Oude Voshaar R, Fernández G, Buitelaar J, Tendolkar I: Amygdala volume marks the acute state in the early course of depression. Biol Psychiatry 2009; 65:812–818Crossref, MedlineGoogle Scholar

19 First MB, Spitzer RL, Gibbon M, Williams JBW: Structured Clinical Interview for DSM-IV Axis I Disorders (SCID), Clinician Version. Washington, DC, American Psychiatric Press, 1996Google Scholar

20 Hamilton M: A rating scale for depression. J Neurol Neurosurg Psychiatry 1960; 23:56–62Crossref, MedlineGoogle Scholar

21 Frank E, Prien RF, Jarrett RB, Keller MB, Kupfer DJ, Lavori PW, Rush AJ, Weissman MM: Conceptualization and rationale for consensus definitions of terms in major depressive disorder: remission, recovery, relapse, and recurrence. Arch Gen Psychiatry 1991; 48:851–855Crossref, MedlineGoogle Scholar

22 Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC: The Mini-International Neuropsychiatric Interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry 1998; 59(suppl 20):22–33MedlineGoogle Scholar

23 Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J: An inventory for measuring depression. Arch Gen Psychiatry 1961; 4:561–571Crossref, MedlineGoogle Scholar

24 Spielberger CD, Gorschuch RL, Lushene RE: STAI Manual for the State Trait Anxiety Inventory. Palo Alto, Calif, Consulting Psychologists Press, 1970Google Scholar

25 Oldfield RC: The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 1971; 9:97–113Crossref, MedlineGoogle Scholar

26 Schmand B, Lindeboom J, van Harskamp F: Nederlandse leestest voor volwassenen: handleiding. Lisse, the Netherlands, Swets & Zeitlinger, 1992Google Scholar

27 Bouma AMulder JLindeboom J (eds): Nederlandse leestest voor volwassenen (NLV). Lisse, the Netherlands, Swets & Zeitlinger, 1996Google Scholar

28 Osterrieth PA: Le test de copie d'une figure complexe: contribution a l'etude de la perception et de la memoire. Arch Psychol 1944; 30:286–356Google Scholar

29 Saan RJ, De Deelman BG: 15-woorden tests A en B: een voorlopige handleiding. Groningen, the Netherlands, University Medical Center Groningen, Department of Neuropsychology, 1986Google Scholar

30 Wechsler D: Wechsler Adult Intelligence Scale, 3rd ed: Administration and Scoring Manual TPC. New York, Oxford University Press, 1997Google Scholar

31 Reitan RM: Validity of the Trail Making Test as an indicator of organic brain damage. Percept Mot Skills 1958; 8:271–276CrossrefGoogle Scholar

32 Heaton RK, Chelune RK, Talley JL, Kay GG, Curtiss G: Wisconsin Card Sorting Test Manual: Revised and Expanded. Odessa, Tex, Psychological Assessment Resources, 1993Google Scholar

33 Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, van der Kouwe A, Jenkins BG, Dale AM, Fischl B: Regional and progressive thinning of the cortical ribbon in Huntington’s disease. Neurology 2002; 58:695–701Crossref, MedlineGoogle Scholar

34 Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B: Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 2006; 32:180–194Crossref, MedlineGoogle Scholar

35 Fischl B, Sereno MI, Tootell RB, Dale AM: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 1999; 8:272–284Crossref, MedlineGoogle Scholar

36 Dale AM, Fischl B, Sereno MI: Cortical surface-based analysis, I: segmentation and surface reconstruction. Neuroimage 1999; 9:179–194Crossref, MedlineGoogle Scholar

37 Lyoo IK, Sung YH, Dager SR, Friedman SD, Lee JY, Kim SJ, Kim N, Dunner DL, Renshaw PF: Regional cerebral cortical thinning in bipolar disorder. Bipolar Disord 2006; 8:65–74Crossref, MedlineGoogle Scholar

38 Ashburner J, Csernansky JG, Davatzikos C, Fox NC, Frisoni GB, Thompson PM: Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol 2003; 2:79–88Crossref, MedlineGoogle Scholar

39 Rauch SL, Wright CI, Martis B, Busa E, McMullin KG, Shin LM, Dale AM, Fischl B: A magnetic resonance imaging study of cortical thickness in animal phobia. Biol Psychiatry 2004; 55:946–952Crossref, MedlineGoogle Scholar

40 Loozen SWHC, Post GJ: Het onderwijsniveau van de Nederlands bevolking: Uitkomsten van de Enquete Beroepsbevolking. Sociaal-economische maandstatistiek 1991; 4:4–13Google Scholar

41 Talairach J, Tournoux P: Co-Planar Stereotaxic Atlas of the Human Brain: Three-Dimensional Proportional System. New York, Thieme Medical, 1988Google Scholar

42 Murray EA, Wise SP, Drevets WC: Localization of dysfunction in major depressive disorder: prefrontal cortex and amygdala. Biol Psychiatry 2011; 69:e43–e54Crossref, MedlineGoogle Scholar

43 Konarski JZ, McIntyre RS, Kennedy SH, Rafi-Tari S, Soczynska JK, Ketter TA: Volumetric neuroimaging investigations in mood disorders: bipolar disorder versus major depressive disorder. Bipolar Disord 2008; 10:1–37Crossref, MedlineGoogle Scholar

44 Kempton MJ, Salvador Z, Munafò MR, Geddes JR, Simmons A, Frangou S, Williams SC: Structural neuroimaging studies in major depressive disorder: meta-analysis and comparison with bipolar disorder. Arch Gen Psychiatry 2011; 68:675–690Crossref, MedlineGoogle Scholar

45 Drevets WC: Orbitofrontal cortex function and structure in depression. Ann NY Acad Sci 2007; 1121:499–527Crossref, MedlineGoogle Scholar

46 Kühn S, Schubert F, Gallinat J: Structural correlates of trait anxiety: reduced thickness in medial orbitofrontal cortex accompanied by volume increase in nucleus accumbens. J Affect Disord 2011; 134:315–319Crossref, MedlineGoogle Scholar

47 Parker G, Wilhelm K, Mitchell P, Austin MP, Roussos J, Gladstone G: The influence of anxiety as a risk to early onset major depression. J Affect Disord 1999; 52:11–17Crossref, MedlineGoogle Scholar

48 Sandi C, Richter-Levin G: From high anxiety trait to depression: a neurocognitive hypothesis. Trends Neurosci 2009; 32:312–320Crossref, MedlineGoogle Scholar

49 Rauch SL, Milad MR, Orr SP, Quinn BT, Fischl B, Pitman RK: Orbitofrontal thickness, retention of fear extinction, and extraversion. Neuroreport 2005; 16:1909–1912Crossref, MedlineGoogle Scholar

50 Milad MR, Quinn BT, Pitman RK, Orr SP, Fischl B, Rauch SL: Thickness of ventromedial prefrontal cortex in humans is correlated with extinction memory. Proc Natl Acad Sci USA 2005; 102:10706–10711Crossref, MedlineGoogle Scholar

51 Terasawa Y, Fukushima H, Umeda S: How does interoceptive awareness interact with the subjective experience of emotion? an fMRI study. Hum Brain Mapp 2013; 34:598–612MedlineGoogle Scholar

52 Olson IR, Plotzker A, Ezzyat Y: The enigmatic temporal pole: a review of findings on social and emotional processing. Brain 2007; 130:1718–1731Crossref, MedlineGoogle Scholar

53 Murai T, Fujimoto S: Rapid cycling bipolar disorder after left temporal polar damage. Brain Inj 2003; 17:355–358Crossref, MedlineGoogle Scholar

54 Mikhailova ES, Vladimirova TV, Iznak AF, Tsusulkovskaya EJ, Sushko NV: Abnormal recognition of facial expression of emotions in depressed patients with major depression disorder and schizotypal personality disorder. Biol Psychiatry 1996; 40:697–705Crossref, MedlineGoogle Scholar

55 Peng J, Liu J, Nie B, Li Y, Shan B, Wang G, Li K: Cerebral and cerebellar gray matter reduction in first-episode patients with major depressive disorder: a voxel-based morphometry study. Eur J Radiol 2011; 80:395–399Crossref, MedlineGoogle Scholar

56 Beauregard M, Paquette V, Lévesque J: Dysfunction in the neural circuitry of emotional self-regulation in major depressive disorder. Neuroreport 2006; 17:843–846Crossref, MedlineGoogle Scholar

57 Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15:273–289Crossref, MedlineGoogle Scholar

58 Phan KL, Fitzgerald DA, Nathan PJ, Moore GJ, Uhde TW, Tancer ME: Neural substrates for voluntary suppression of negative affect: a functional magnetic resonance imaging study. Biol Psychiatry 2005; 57:210–219Crossref, MedlineGoogle Scholar

59 Drevets WC, Savitz J, Trimble M: The subgenual anterior cingulate cortex in mood disorders. CNS Spectr 2008; 13:663–681Crossref, MedlineGoogle Scholar

60 Bora E, Fornito A, Pantelis C, Yücel M: Gray matter abnormalities in major depressive disorder: a meta-analysis of voxel based morphometry studies. J Affect Disord 2012; 138:9–18Crossref, MedlineGoogle Scholar

61 Hasler G, Northoff G: Discovering imaging endophenotypes for major depression. Mol Psychiatry 2011; 16:604–619Crossref, MedlineGoogle Scholar

62 Gould E, Reeves AJ, Graziano MS, Gross CG: Neurogenesis in the neocortex of adult primates. Science 1999; 286:548–552Crossref, MedlineGoogle Scholar

63 Ohira K: Injury-induced neurogenesis in the mammalian forebrain. Cell Mol Life Sci 2011; 68:1645–1656Crossref, MedlineGoogle Scholar

64 Ohira K, Takeuchi R, Shoji H, Miyakawa T: Fluoxetine-induced cortical adult neurogenesis. Neuropsychopharmacology 2013; 38:909–920Crossref, MedlineGoogle Scholar

65 Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, Mintun MA, Wang S, Coalson RS, Raichle ME: The default mode network and self-referential processes in depression. Proc Natl Acad Sci USA 2009; 106:1942–1947Crossref, MedlineGoogle Scholar

66 Li B, Liu L, Friston KJ, Shen H, Wang L, Zeng LL, Hu D: A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry (Epub ahead of print, Jan 2, 2013)Google Scholar