Shared and Unique Changes in Brain Connectivity Among Depressed Patients After Remission With Pharmacotherapy Versus Psychotherapy
Publication: American Journal of Psychiatry
Abstract
Objective:
The authors sought to determine the shared and unique changes in brain resting-state functional connectivity (rsFC) between patients with major depressive disorder who achieved remission with cognitive-behavioral therapy (CBT) or with antidepressant medication.
Methods:
The Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) trial randomized adults with treatment-naive major depressive disorder to 12 weeks of treatment with CBT (16 1-hour sessions) or medication (duloxetine 30–60 mg/day or escitalopram 10–20 mg/day). Resting-state functional MRI scans were performed at baseline and at week 12. The primary outcome was change in the whole-brain rsFC of four seeded brain networks among participants who achieved remission.
Results:
Of the 131 completers with usable MRI data (74 female; mean age, 39.8 years), remission was achieved by 19 of 40 CBT-treated and 45 of 91 medication-treated patients. Three patterns of connectivity changes were observed. First, those who remitted with either treatment shared a pattern of reduction in rsFC between the subcallosal cingulate cortex and the motor cortex. Second, reciprocal rsFC changes were observed across multiple networks, primarily increases in CBT remitters and decreases in medication remitters. And third, in CBT remitters only, rsFC increased within the executive control network and between the executive control network and parietal attention regions.
Conclusions:
Remission from major depression via treatment with CBT or medication is associated with changes in rsFC that are mostly specific to the treatment modality, providing biological support for the clinical practice of switching between or combining these treatment approaches. Medication is associated with broadly inhibitory effects. In CBT remitters, the increase in rsFC strength between networks involved in cognitive control and attention provides biological support for the theorized mechanism of CBT. Reducing affective network connectivity with motor systems is a shared process important for remission with both CBT and medication.
Major depressive disorder is a prevalent and debilitating condition treated with two equally efficacious first-line treatments: antidepressant medication or evidence-based psychotherapy, such as cognitive-behavioral therapy (CBT) (1). The goal of the acute phase of treatment for major depression is remission (2), which reflects a return to a high level of psychosocial functioning and is associated with a reduced risk of depressive relapse (3, 4). CBT and medication treatment of outpatients with major depression yield similar overall remission rates, but there are clearly individual patients who respond to one form of treatment but not the other (5, 6). Although the two forms of treatment are conceptualized to yield improvements via highly differing actions in the brain (7), there is a dearth of empirical data relevant to and supporting these hypothesized models. Only a few small studies have contrasted the changes in brain metabolism or blood flow associated with the two treatments (8–11). Improved understanding of treatment-specific effects will help personalize treatments for major depression and inform its longitudinal management.
The past decade of neuroimaging research has revealed distributed brain networks whose activity is associated with specific functions. Four specific networks have been consistently implicated in the pathophysiology of major depressive disorder (12). The default mode network (DMN) primarily comprises the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC)/precuneus, and bilateral inferior parietal lobes; it is the dominant network during internally focused activity (13–16). The executive control network (ECN; also known as the cognitive control network or fronto-parietal network) includes the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), and posterior parietal cortex (13). The ECN is engaged during externally focused tasks and is believed to contribute to regulation of emotion through control over limbic regions (17). The salience network (SN) (broadly similar to the ventral attention network) comprises the ACC, anterior insula, and several subcortical regions, including the amygdala, hypothalamus, ventral striatum, thalamus, and specific brainstem nuclei involved in emotion processing and regulation. This network detects behaviorally relevant stimuli, including ascending interoceptive signals processed through the posterior and mid-insula, which are integrated in the fronto-insular cortex (18). The SN coordinates neural responses to salient stimuli by directing the switch between ECN- and DMN-predominant activity (19) and regulating viscero-autonomic activity (20) through descending projections to nuclei in the brainstem and spinal cord (21). Finally, the subcallosal cingulate cortex (SCC), part of the affective (limbic) network (AN), is a key hub for the processing of affective states and is structurally connected to the ventral striatum, midline thalamus, dorsal raphe, ACC, mPFC, and hippocampus (22, 23).
Resting-state functional connectivity (rsFC), a measure of the temporal synchronization of the blood-oxygen-level-dependent signal across brain regions when a participant is at rest in an MRI scanner, has helped identify the functional networks of the brain. Although several previous studies have examined changes in brain connectivity before and after treatment for major depressive disorder, this body of work suffers from limitations related to small samples evaluating a single treatment modality and/or a single network (24). No previous studies have contrasted multiple network changes occurring among psychotherapy- and antidepressant-treated patients. In addition, previous studies have primarily included patients with varying levels of prior treatment, a source of potential heterogeneity (24). Here, we report on the changes in rsFC of four major brain networks among treatment-naive patients with major depression who remitted after 12 weeks of CBT or antidepressant medication.
Using seeds for the DMN, ECN, SN, and AN, we addressed the following questions regarding change in rsFC from baseline to week 12:
1.
What changes are common across patients who achieve remission, irrespective of their specific treatment?
2.
What changes are unique to patients who achieve remission with CBT versus with an antidepressant medication?
3.
Do the patterns of patients who remitted after treatment differ from those of healthy, never-depressed control subjects?
Our primary interest was the differential effects of the two treatments. Based on putative mechanisms of the treatments (7) and prior studies (24), we hypothesized that remission with CBT would be associated with enhanced connectivity of the ECN. Based on small studies that found reductions of DMN connectivity after antidepressant medication treatment (25–28), we hypothesized that patients who remitted with pharmacotherapy would evince reduced intra-DMN connectivity.
Methods
Study Design and Participants
The Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study was a randomized controlled trial. The study protocol (29) and clinical outcomes (30) have been published and are summarized in the online supplement. Briefly, PReDICT was conducted at Emory University and at Grady Hospital in Atlanta from 2007 to 2013 and was approved by Emory University’s Institutional Review Board and the Grady Hospital Research Oversight Committee. All participants provided written informed consent to participate. PReDICT was designed to find predictors and moderators of outcomes for three first-line treatments for major depressive disorder. The study enrolled treatment-naive outpatients ages 18–65 years with a current major depressive episode. Key eligibility criteria included a score ≥18 at screening and ≥15 at baseline on the 17-item Hamilton Depression Rating Scale (HAM-D) (31) and fluency in English or Spanish. Key exclusion criteria included a lifetime diagnosis of bipolar disorder, schizophrenia, or dementia, any medically significant or unstable medication condition, current eating disorder, obsessive-compulsive disorder, and substance abuse or dependence as assessed by the Structured Clinical Interview for DSM-IV Axis I Disorders (32). Participants were randomized in a 1:1:1 ratio to 12 weeks of treatment with duloxetine (30–60 mg/day), escitalopram (10–20 mg/day), or CBT (16 individual 1-hour sessions) (33). Concomitant treatment with other psychotropic medications was prohibited, except for non-benzodiazepine sedatives, which could be taken up to three times per week, but not the night before scanning visits. The primary outcome for the trial was remission, defined as having a HAM-D score ≤7 at both weeks 10 and 12. Other outcomes assessed were response without remission (defined as a reduction ≥50% in HAM-D score from baseline to week 12 and a score >7 at week 10 or 12), and nonresponse, defined as a reduction of <50% in HAM-D score from baseline to week 12.
Thirty-five healthy control subjects underwent structural and functional MRI (fMRI) scanning on the same scanner using an identical imaging protocol. These participants were recruited as part of separate imaging studies at Emory University and were screened for a history of current or past neurological and psychiatric illness.
fMRI Acquisition and Analysis
Patients completed resting-state fMRI scans during the week prior to the baseline (randomization) visit and during the 1–4 days prior to the week-12 (final outcome) visit. Bilateral spheres of 5-mm radius were used as seeds for each of the four networks, based on prior research (see Figure S1 in the online supplement), and the mean time course for each seed was correlated voxel-wise with the rest of the brain. To maximize the power of the comparison with CBT, we combined the two medication groups into one, consistent with our previous neuroimaging PReDICT analysis (34). We used Yeo and colleagues’ 17-network atlas (35) to interpret the rsFC analyses. Details of fMRI acquisition, preprocessing, and analysis are provided in the online supplement.
Statistical Analysis
Linear mixed-effects models with random intercepts for patient factors were used to examine whether there were significant time effects, treatment effects, or interaction effects between treatment and time. Although age has been associated previously with differential outcome to treatment with medication versus CBT (36), age was not associated with treatment outcome in PReDICT, and including age as a covariate in the model did not significantly change the functional connectivity results, so the final model did not include age. The models were first run for all remitters, looking for shared and unique change patterns by treatment. Models were run using permutation analyses to reduce the impact of outliers and false positive clusters. Permutation consisted of whole-brain voxel-wise subsampling run 1,000 times with 70% random subsamples, as applied previously (34). The subsampling maintained the proportional treatment grouping of the overall cohort. The resulting F-maps for each linear mixed-effects model utilized an alpha of 0.005, and a beta of 0.80 was chosen to keep voxels with a minimum of 80% power. For the significant time effects, post hoc t tests between pairs of time points were performed to illustrate change direction. Additionally, post hoc t tests between each time point and the healthy control subjects were performed to examine how patients’ pretreatment and posttreatment rsFC states compared to those of the healthy control subjects. Lastly, post hoc contrast tests between time-by-treatment pairs were also performed for the interaction effect. For each significant region identified in the remitter analysis, correlation analyses between the rsFC and clinical improvement were performed using all treatment completers.
To evaluate whether shared changes were specific to remitters or may have been due simply to 12 weeks of treatment, we conducted three comparative analyses using all completers regardless of outcome. First, we analyzed changes in rsFC for each of the four seeds while incorporating percent change in HAM-D score as a covariate. Next, due to the shared SCC rsFC changes identified in the remitter analyses (see Results), we evaluated change in rsFC using the SCC seed for all completers, using response versus nonresponse as a covariate. Finally, using the specific motor cluster identified in the shared SCC rsFC change among remitters, we evaluated all completers for change in this rsFC pattern using the outcomes of week-12 HAM-D score and percent change in HAM-D score, and also by comparing rsFC change across the categorical outcomes of remission, response without remission, and nonresponse.
Results
Participants
A total of 131 patients completed treatment and had usable MRI scans at both baseline and week 12. Table 1 summarizes the demographic and clinical characteristics of the sample; the mean age was 39.8 years (SD=11.26), and 56.5% were female. Among these completers, 45 of 91 (49.5%) patients treated with medication and 19 of 40 (47.5%) patients treated with CBT achieved remission. The healthy control subjects were demographically similar to the patients with major depression; the mean age was 36.7 years (SD=8.69), and 60% were female.
| Study Group | ||||||||
|---|---|---|---|---|---|---|---|---|
| Characteristic | Antidepressant Group (N=91) | CBT Group (N=40) | All Patients (N=131) | Control Subjects (N=35) | ||||
| Mean | SD | Mean | SD | Mean | SD | Mean | SD | |
| Age (years) | 40.5 | 11.5 | 38.3 | 11.0 | 39.8 | 11.4 | 36.7 | 8.7 |
| N | % | N | % | N | % | N | % | |
| Female | 51 | 56.0 | 23 | 57.5 | 74 | 56.5 | 21 | 60.0 |
| Hispanic | 19 | 20.9 | 8 | 20.0 | 27 | 20.6 | ||
| Raceb | ||||||||
| White | 51 | 56.0 | 28 | 70.0 | 79 | 60.3 | ||
| Black | 18 | 19.8 | 2 | 5.0 | 20 | 15.3 | ||
| Native American | 15 | 16.5 | 7 | 17.5 | 22 | 16.8 | ||
| Asian | 2 | 2.2 | 0 | 0.0 | 2 | 1.5 | ||
| More than one race | 5 | 5.5 | 2 | 5.0 | 7 | 5.3 | ||
| Education >high school | 74 | 81.3 | 32 | 80.0 | 106 | 80.9 | ||
| Married or cohabitating | 49 | 53.8 | 24 | 60.0 | 73 | 55.7 | ||
| Employed | 60 | 65.9 | 26 | 65.0 | 86 | 65.6 | ||
| Number of lifetime episodes | ||||||||
| 1 | 47 | 51.6 | 24 | 60.0 | 71 | 54.2 | ||
| 2 | 13 | 14.3 | 4 | 10.0 | 17 | 13.0 | ||
| ≥3 | 31 | 34.1 | 12 | 30.0 | 43 | 32.8 | ||
| Chronic episode (≥2 years)c | 30 | 33.0 | 12 | 30.0 | 42 | 32.1 | ||
| Current anxiety disorder | 39 | 42.9 | 18 | 45.0 | 57 | 43.5 | ||
| Mean | SD | Mean | SD | Mean | SD | |||
| HAM-D score | 18.9 | 3.2 | 18.5 | 3.1 | 18.8 | 3.2 | ||
| HAM-A score | 15.3 | 4.6 | 15.0 | 4.0 | 15.2 | 4.4 | ||
a
There were no statistically significant differences (all p values >0.2) between medication- and CBT-treated patients on any of the demographic or clinical variables, and no significant difference between the major depression and healthy control groups by age or sex. CBT=cognitive-behavioral therapy; HAM-D=Hamilton Depression Rating Scale; HAM-A=Hamilton Anxiety Rating Scale.
b
One participant in the CBT group had missing data for race.
c
One participant in the medication group had missing data for episode duration.
Shared Changes in Functional Connectivity Across All Remitters
Patients who remitted with CBT and medication demonstrated only one statistically significant common change in rsFC: reduced connectivity of the SCC with the left medial motor cortex (mM1, centered on x=−8, y=−41, z=68), as shown in Figure 1A. As indicated in Table 2, the rsFC change in these voxels was specific to remitters, being nonsignificant in patients with the outcomes of nonresponse and response without remission. The specificity of this SCC-mM1 rsFC change for these voxels in remitters was further demonstrated from analyses using HAM-D score as a continuous outcome measure across all treatment completers, which showed no significant correlation between this SCC-mM1 rsFC change with percent change in HAM-D score (R2=0.015, p=0.16) or with week-12 HAM-D score (R2=0.02, p=0.11). Additional analyses examining whole-brain rsFC changes for all four seeds among all completers and using percent change in HAM-D score as a covariate found no significant overlapping voxels common to both treatments (data not shown). Finally, Figure 1B shows another comparative analysis using all completers, this time examining permuted whole-brain changes in rsFC of the SCC seed within each treatment group using responder status as a covariate. Here, both treatments reduced SCC-mM1 rsFC, although in this all-completers analysis, no overlapping voxels common to both treatments were identified. Taken together, these results indicate that reduction in SCC-mM1 rsFC may be a common effect of 12 weeks of treatment and that in remitters specifically, this effect was larger and convergent between CBT and medication.
FIGURE 1. Shared and unique changes in resting-state functional connectivity (rsFC) among patients treated with CBT or antidepressant medicationa
aAs shown in panel A, shared change in rsFC from baseline to week 12 occurred in both CBT- and medication-treated remitters. The inset is an enlargement of the shared region in the mM1 showing the specific voxels with significant change in remitters treated with medication (red), with CBT (green), and their overlapping region (yellow). The violin plot shows the similar reductions in both medication and CBT remitters in the overlap cluster, and that at week 12 the SCC-mM1 connectivity in remitted patients was significantly lower than that of healthy control subjects. Panel B shows changes in rsFC of the SCC from baseline to week 12 occurring in CBT and medication completers while controlling for response. Both treatments are associated with reductions in SCC-mM1 rsFC, although there are no overlapping voxels. The violin plots demonstrate the nonsignificant change in the medication-treated patients when analyzing the center of mass in the significant CBT mM1 cluster, and the nonsignificant change in the CBT-treated patients when analyzing the center of mass in the significant medication mM1 cluster. Panel C shows the unique changes in rsFC from baseline to week 12 that differed significantly between CBT and medication remitters. Centers of mass and cluster size for each region are provided in Table 3. Green represents CBT-treated patients, red represents medication-treated patients, and blue represents healthy control subjects. Solid green or red lines represent statistically significant changes (p<0.005) in rsFC within a treatment group from baseline to week 12; dashed green or red lines represent 0.005<p<0.05. Statistically significant differences between patients and healthy control subjects are indicated by dotted (p<0.05) or solid (p<0.01) blue lines at the top of the plots. Statistically significant differences between treatment groups, either at baseline or at week 12, are indicated by dotted (p<0.05) or solid (p<0.01) black lines at the bottom of the plots. Med=antidepressant medication; CBT=cognitive-behavioral therapy; DLPFC=dorsolateral prefrontal cortex; mM1=medial motor cortex; PCC=posterior cingulate cortex; SCC=subcallosal cingulate cortex.
| Treatment and Outcome Group | t | p | Cohen’s d |
|---|---|---|---|
| All patients | |||
| Remission | 6.07 | <0.001 | 0.759 |
| Response without remission | 1.64 | 0.113 | 0.310 |
| Nonresponse | 1.15 | 0.257 | 0.184 |
| CBT group | |||
| Remission | 4.66 | <0.001 | 1.068 |
| Response without remission | −1.44 | 0.211 | −0.586 |
| Nonresponse | 0.71 | 0.492 | 0.182 |
| Antidepressant group | |||
| Remission | 4.42 | <0.001 | 0.659 |
| Response without remission | 2.02 | 0.057 | 0.430 |
| Nonresponse | 0.89 | 0.382 | 0.182 |
a
The left medial motor cortex was centered on x=−8, y=−41, z=68.
Analyses of the DMN, ECN, and SN seeds revealed no significant rsFC changes common to the treatments.
Treatment-Specific Changes in Functional Connectivity Associated With Remission
Tables S1–S4 in the online supplement show the permuted within-treatment changes in rsFC among patients who remitted with CBT or medication for the four seeds. Figure 1C and Table 3 present the differential rsFC changes between CBT remitters and medication remitters.
| Seed (Network) | Region | MNI Coordinates (Center of Mass) | Cluster Size (mm3) | ||
|---|---|---|---|---|---|
| x | y | z | |||
| DLPFC (ECN) | L inferior parietal lobule | −42.2 | −38.9 | 41.0 | 1,617 |
| R superior parietal lobule | 27.5 | −57.8 | 47.4 | 195 | |
| L superior parietal lobule | −21.8 | −59.6 | 51.6 | 32 | |
| SCC (AN) | R posterior insula | 35.4 | −29.2 | 14.2 | 172 |
| Anterior insula (SN) | L precuneus | −6.9 | −51.6 | 63.8 | 197 |
| L V1 | −19.6 | −75.9 | 8.4 | 191 | |
a
AN=affective (limbic) network; DLPFC=dorsolateral prefrontal cortex; ECN=executive control network; SCC=subcallosal cingulate cortex; SN=salience network.
Default Mode Network
Using the PCC seed, no voxels were identified demonstrating differential rsFC change in DMN connectivity between medication and CBT remitters. However, differential change in rsFC between the PCC and two large, nonoverlapping clusters in the ECN was identified, with CBT remitters showing increased PCC–superior frontal cortex rsFC and medication remitters showing reduced PCC–middle frontal cortex rsFC (see Table S2 in the online supplement).
Executive Control Network
The ECN showed significant differential changes in rsFC in regions involving attention and cognitive control, most notably between the DLPFC and the left inferior parietal lobule (Yeo control A network). At baseline, the rsFC between these regions was significantly lower in the CBT remitters than the medication remitters. However, after treatment this difference inverted, with rsFC increased in the CBT group and decreased in the medication group (Figure 1C).
A similar pattern was found between the DLPFC seed and bilateral superior parietal regions. The rsFC significantly decreased from baseline to week 12 in medication remitters and increased in CBT remitters; furthermore, at week 12 the rsFC of the two remitter groups also differed significantly. CBT remitters had lower rsFC at baseline with these bilateral parietal regions, although this difference was only statistically significant on the right side (Figure 1C).
Affective Network
AN rsFC with the posterior insula (Yeo somatomotor B network) significantly increased in CBT remitters, but no significant change occurred in medication remitters. At baseline, the rsFC was significantly lower in the eventual CBT remitters compared with medication remitters, but by week 12 it was significantly higher in the CBT remitters (Figure 1C).
Salience Network
The rsFC of the SN demonstrated significantly different changes between the medication and CBT remitters in rsFC with V1 in the occipital cortex (Yeo visual B network): the rsFC in CBT remitters significantly increased, whereas in medication remitters it significantly decreased. This rsFC differed significantly at baseline and at week 12 between the remitter groups (Figure 1C).
A second region that differentially changed in SN rsFC between CBT and medication remitters was the left precuneus (Yeo dorsal attention B). The two remitter groups differed significantly at baseline and at week 12, with increasing rsFC in the CBT remitters and decreasing rsFC in the medication remitters, thus exhibiting a change pattern similar to the SN–visual B rsFC (Figure 1C).
Comparison With Healthy Control Subjects
At baseline, the patients who eventually remitted with either CBT or medication both demonstrated nonsignificantly greater AN connectivity to mM1 than the healthy control subjects. After remission was achieved, the strength of the AN-mM1 rsFC was reversed, with the remitters showing significantly lower connectivity than the healthy control subjects (Figure 1A).
Among the networks implicated in differential change by treatment group, the most striking finding was that at week 12, the networks’ rsFC with several regions involved in cognitive control and attention was significantly higher in CBT remitters than in the healthy control subjects. At endpoint, CBT remitters showed significantly elevated ECN–inferior parietal rsFC as well as SN-precuneus and SN–posterior insula rsFC (Figure 1C). This pattern of results suggests that benefit from CBT depends on effectively strengthening the role of attentional control in response to salient external or interoceptive stimuli. For medication remitters, none of the week-12 treatment-specific rsFC patterns differed significantly from those of the healthy control subjects (Figure 1C).
Treatment-Specific Changes in rsFC Across All Completers
To determine whether the connectivity of regions identified as specifically changing in CBT or medication remitters reflected a continuum of change with improvement, we conducted two analyses. First, we analyzed change in rsFC for each of the six interaction change patterns from Figure 1C by outcome group for each treatment (see Table S5 in the online supplement); we found no significant associations of change in these rsFC patterns in nonresponders. Second, we plotted the percent improvement in HAM-D score across all completers (see Figure S2 in the online supplement). The ECN–left inferior parietal connectivity (see Figure S2 in the online supplement) did not show a significant interaction between treatments, and there were no significant correlations between change in rsFC of these regions with change in depressive symptoms with each treatment. That these regions showed a significant effect in remitters but not in the entire sample suggests that a certain threshold of connectivity change of these regions was required to reach the remitted state.
Four other network connectivities that differentially changed between CBT and medication remitters showed significant interactions in the entire sample, although in each case the interaction was associated with a significant correlation within only one treatment group (see Figure S2 in the online supplement). These significant correlations all involved the insula or the superior parietal lobule, again emphasizing the importance of the treatments in modulating salience and attention processing.
Change in Treatment Prediction Biomarker
As a separate analysis, we examined whether remitters demonstrated changes in the treatment prediction biomarker that was previously identified in this sample (34), namely, the sum of rsFC of the SCC to three left-side regions: midbrain, ventrolateral PFC/anterior insula, and ventromedial PFC. As shown in Figure S3 in the online supplement, there was a significant (p<0.003) time-by-treatment interaction for the summed z-score, driven by a significant reduction in the CBT remitter group but no change in the medication remitter group.
Discussion
This study directly compared the changes in brain network connectivity in patients with major depressive disorder who were successfully treated to remission with antidepressant medication or CBT. The only statistically significant shared change among remitters regardless of treatment was a reduction in rsFC between the AN and the somatomotor network, particularly the medial aspect of M1 in the trunk section of Penfield’s original homunculus (37). In contrast, the two treatments differed significantly in their effects on patterns of rsFC of the ECN, AN, and SN. Most notably, CBT remitters demonstrated enhanced connectivity of the ECN with attention regions, which did not occur in medication remitters, a change consistent with theorized mechanisms of action of CBT on the brain (7). Additionally, whereas CBT remitters acquired greater connectivity of the AN to the posterior insula, and of the SN to the precuneus and occipital visual regions, medication remitters evinced reduced strength of these regions’ connectivity. Figure 2 presents a schematic of the differential change patterns among CBT remitters and medication remitters.
FIGURE 2. Schematic of brain connectivity changes with remission from depression after treatment with CBT or antidepressant medicationa
aPanel A shows changes in functional connectivity occurring in patients who remitted with CBT, and panel B shows changes in functional connectivity occurring in patients who remitted with antidepressant medication treatment. Schematic diagrams are based on results from Figure 1 and Table S2 in the online supplement. Green areas demarcate the regions showing functional connectivity changes. The dark blue arc connecting the SCC to the somatomotor cortex in the diagrams indicates that both CBT-treated and medication-treated remitters demonstrated reductions in connectivity between these regions. The red arcs in the CBT schematic indicate increases in connectivity from baseline to week 12 among CBT remitters, and the light blue arcs in the antidepressant medication schematic indicate decreases in connectivity occurring among medication remitters. ANT INS=anterior insula; CBT=cognitive-behavioral therapy; DLPFC=dorsolateral prefrontal cortex; PCC=posterior cingulate cortex; SCC=subcallosal cingulate cortex.
It is important to recognize that our pre-post resting-state fMRI analyses cannot prove directionality or causality of rsFC effects. While acknowledging that caveat, the difficulty patients with major depression report in converting thoughts into physical actions (e.g., getting out of bed) may reflect a limbic inhibition of motor activation, and the finding of reduced SCC-somatomotor connectivity in remitters suggests the potential importance of reducing limbic input to motor regions as a mechanism for recovery from major depression. The primary motor cortex, including medial M1, is one of the “rich club hubs” in the brain, a moniker referring to its dense interconnectivity to hubs of other functional resting-state networks (38). The primary motor cortex contributes to the integration of information between segregated functional domains, including the affective network. Moreover, recent work has found that primary motor neurons and the SCC both project to the adrenal medulla, potentially representing the “central command” for preparing the body to meet anticipated metabolic needs in the face of stress (39). The colocalization of the control of this visceromotor function with skeletomotor function facilitates their coordinated responses, potentially linking the affective, motor, and neuroendocrine disturbances observed in major depression (40). The importance of these findings for major depression is further supported by recent breakthroughs demonstrating that the motor cortex in primates, particularly medial M1, has undergone major organizational changes through evolution to achieve greater connections with the mid-cingulate cortex (41), itself an important network hub involved in cost-benefit computations about potential physical actions (42). The reduction in rsFC between the AN and the medial motor cortex observed in patients who remitted with both treatments suggests that reducing limbic input to motor regions may be a crucial aspect of recovery from major depression.
Other pre-post fMRI studies of improvement with medication have also identified reduced activity within the somatomotor network, including the bilateral precentral gyrus (M1) with serotonin-norepinephrine reuptake inhibitors (SNRIs) (43), as well as reduced somatomotor network connectivity with other networks after treatment with a selective serotonin reuptake inhibitor (SSRI) (44). Korgaonkar and colleagues (45) recently demonstrated that increased somatomotor–ventral attention network rsFC occurred only in patients who were nonremitters with SSRI or SNRI treatment. A recent study of rsFC change in CBT-treated patients with major depression (46) found that treatment reduced connectivity between the SCC and the paracentral lobule, which includes motor and premotor regions. Although that study reported no significant SCC rsFC change pattern associated with symptom improvement, a secondary analysis using a more relaxed threshold found a correlation between reduction in symptom severity and reduction in SCC–supplementary motor area rsFC. Another study of 20 sessions of intermittent theta burst stimulation (47) found that only responders demonstrated significant reductions in rsFC between the SCC seed and the premotor cortex of Brodmann area 6. It is also possible that the efficacy of exercise as a treatment for major depression involves this network (48), although no imaging data have addressed this question. Taken together, the evidence indicates that across multiple types of treatment, reduced limbic-motor connectivity is a generalized aspect of improvement in depressive symptoms.
Our hypothesis that CBT would increase ECN rsFC was supported by the most robust findings from this analysis: large and extensive CBT-specific increases in connectivity occurred between the DLPFC and parietal regions involved in cognitive control and attention (49), consistent with the importance of this network for major depression (12). Furthermore, when evaluating across all CBT completers regardless of outcome, we found a significant positive correlation between percent reduction in HAM-D score and increase in ECN rsFC with the left superior parietal lobule (see Figure S2 in the online supplement) (3), a finding that indicates that clinical improvement is proportional to the degree of engagement of this network. Other groups have demonstrated left ECN engagement during active reappraisal of emotional meaning while attending to negative stimuli (50, 51); reappraisal is a core component of CBT (33). These imaging findings align well with the cognitive theory of depression, which considers impairment in control over mood-congruent material to be a core deficit of major depression that sustains the depressed mood (52). Poor inhibition of or disengagement from negatively valenced material in working memory is believed to contribute to the negative attention bias, negative memory bias, and maladaptive coping strategies that are common in patients with major depression (53). Taken together, these data suggest that CBT cognitive restructuring procedures enhance the connectivity of the brain networks thought to underlie these core clinical features and that lower ECN rsFC may be an intervention target for CBT. It is also notable that at week 12 of our study, compared with healthy control subjects, CBT remitters had significantly greater rsFC between the ECN and inferior parietal attentional regions. This result may reflect the effect of repetitive practice of cognitive restructuring exercises, or that patients who are capable of remitting with CBT have inherently greater connectivity than healthy control subjects between these networks when not depressed, which is only revealed upon restoration of wellness. Additional studies comparing healthy control subjects and patients with major depression who had previously remitted with CBT could further inform this question.
Patients with major depression also demonstrate greater rsFC within the DMN and between the DMN and the ECN than healthy control subjects (12, 54), informing our second hypothesis that remission with antidepressant medication treatment would be associated with reductions in DMN within-network rsFC (25–28). As have other investigators (44, 45), we found that improvement with medication was associated with broad reductions in rsFC within and between multiple networks. However, we found little evidence for a medication effect on reducing intra-DMN rsFC, although rsFC between the DMN and ECN was reduced in medication remitters. This null result is consistent with larger and more recent studies that have also failed to find significant reductions in intra-DMN rsFC among medication-treated patients (44, 45, 55–57). The preponderance of evidence now suggests that although increased intra-DMN rsFC distinguishes depressed individuals from healthy control subjects (12), neither medication nor CBT significantly changes connectivity within the DMN. If this conclusion is accurate, then heightened activity in the DMN may be a trait rather than a state marker and possibly a vulnerability factor for major depression.
The finding that the rsFC of visual processing areas with the SN changes as patients achieve remission may reflect changes in the biological systems underlying the negative bias observed in patients with major depression (58). The changes in visual cortex connectivity are consistent with rodent research demonstrating that fluoxetine enhances experience-dependent plasticity in the visual cortex; this enhancement is associated with the SSRI’s modulation of both the concentrations of brain-derived neurotrophic factor and the cortical excitatory/inhibitory balance, important regulators of neuronal plasticity in adult humans (59, 60).
There are few published studies evaluating pre-post rsFC changes associated with improvement with other forms of depression treatment, such as ECT (61), repetitive transcranial magnetic stimulation (rTMS) (62), and ketamine (63), and almost none of these studies have evaluated changes in SCC connectivity in treatment remitters. Our finding of treatment-specific effects within the ECN for CBT and antidepressant medication raises the question of whether rTMS, a treatment targeting the DLPFC, induces rsFC changes within this network; however, a recent review found no significant pre-post rTMS ECN changes across several studies (62). In contrast, studies have demonstrated reduced SCC-DLPFC and SCC–dorsomedial PFC rsFC after response to rTMS (62). A complicating factor for studies using highly targeted treatments, such as rTMS and deep brain stimulation, is that rsFC changes may vary with the accuracy with which the targeted region is stimulated, which can be inconsistent even with the use of fMRI-defined targeting methods (64).
The present study has several strengths, including the large sample size comparing two first-line treatments for major depression and whole-brain voxel-wise analyses. A potential limitation to generalizability is that the sample was treatment naive. The study lacked a placebo arm, which could have informed the degree to which the rsFC changes represent inactive treatment effects. Another limitation, affecting most individual-level fMRI studies, was the inability to assess the stability of the FC mapping (64), which might be addressed in future studies through use of emerging techniques, such as multi-echo fMRI (65). Our analytic method employed an exploratory whole-brain analysis using nodes that are important for specific brain functions relevant to the symptomatology of major depressive disorder (12). Future analyses could adopt a hypothesis-driven approach evaluating the degree to which CBT and antidepressant medication impact the connectivity within and between these nodes. Such an approach may reflect correction or “normalization” of aspects of abnormal connectivity believed to differentiate patients with major depression from healthy control subjects.
This work advances our understanding of how the brain changes as it emerges from depression, and it demonstrates the unique, treatment-specific changes in rsFC that are associated with remission after treatment with medication or CBT. The findings provide biological support for the common evidence-based clinical practice (5, 6) of combining medications and CBT and of switching between modalities. It is likely that failure to engage the ECN during CBT will predict failure of that treatment modality; how soon such engagement could be evaluated to justify a change of treatment is a future research question. The differential changes between CBT and medication remitters in connectivity of the insula with the SCC and dorsal attention networks are consistent with our previous work implicating the activity of the anterior insula with differential outcomes with the two forms of treatment (34). Finally, when considered along with the preliminary findings of functional connectivity changes occurring with other forms of treatment, such as rTMS (62) and ketamine (63), there is an emerging case to be made that any particular form of treatment can only work effectively (i.e., induce changes in brain function leading to remission) if the brain state at baseline is appropriate for that treatment (34). The findings that all remitters showed lower SCC-mM1 rsFC at week 12 than healthy control subjects, and that CBT remitters specifically had higher week-12 ECN rsFC than healthy control subjects, may suggest that patients with remitted major depression do not necessarily return to a “normal” level of connectivity but rather enter an adaptive state within certain networks. Deeper understanding of these unique treatment-specific baseline and change signatures may enable further progress in personalizing treatment for major depression.
Acknowledgments
The authors acknowledge the contribution of Bona Kim for her artwork in Figure 2.
Footnote
ClinicalTrials.gov identifier: NCT00360399.
Supplementary Material
File (appi.ajp.21070727.ds001.pdf)
- View/Download
- 829.70 KB
References
1.
Weitz ES, Hollon SD, Twisk J, et al: Baseline depression severity as moderator of depression outcomes between cognitive behavioral therapy vs pharmacotherapy: an individual patient data meta-analysis. JAMA Psychiatry 2015; 72:1102–1109
2.
Keller MB: Remission versus response: the new gold standard of antidepressant care. J Clin Psychiatry 2004; 65(suppl 4):53–59
3.
Judd LL, Akiskal HS, Maser JD, et al: Major depressive disorder: a prospective study of residual subthreshold depressive symptoms as predictor of rapid relapse. J Affect Disord 1998; 50:97–108
4.
Dunlop BW, Holland P, Bao W, et al: Recovery and subsequent recurrence in patients with recurrent major depressive disorder. J Psychiatr Res 2012; 46:708–715
5.
Schatzberg AF, Rush AJ, Arnow BA, et al: Chronic depression: medication (nefazodone) or psychotherapy (CBASP) is effective when the other is not. Arch Gen Psychiatry 2005; 62:513–520
6.
Dunlop BW, LoParo D, Kinkead B, et al: Benefits of sequentially adding cognitive-behavioral therapy or antidepressant medication for adults with nonremitting depression. Am J Psychiatry 2019; 176:275–286
7.
DeRubeis RJ, Siegle GJ, Hollon SD: Cognitive therapy versus medication for depression: treatment outcomes and neural mechanisms. Nat Rev Neurosci 2008; 9:788–796
8.
Martin SD, Martin E, Rai SS, et al: Brain blood flow changes in depressed patients treated with interpersonal psychotherapy or venlafaxine hydrochloride: preliminary findings. Arch Gen Psychiatry 2001; 58:641–648
9.
Saxena S, Brody AL, Ho ML, et al: Cerebral metabolism in major depression and obsessive-compulsive disorder occurring separately and concurrently. Biol Psychiatry 2001; 50:159–170
10.
Goldapple K, Segal Z, Garson C, et al: Modulation of cortical-limbic pathways in major depression: treatment-specific effects of cognitive behavior therapy. Arch Gen Psychiatry 2004; 61:34–41
11.
Kennedy SH, Konarski JZ, Segal ZV, et al: Differences in brain glucose metabolism between responders to CBT and venlafaxine in a 16-week randomized controlled trial. Am J Psychiatry 2007; 164:778–788
12.
Kaiser RH, Andrews-Hanna JR, Wager TD, et al: Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry 2015; 72:603–611
13.
Fox MD, Snyder AZ, Vincent JL, et al: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005; 102:9673–9678
14.
Greicius MD, Flores BH, Menon V, et al: Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry 2007; 62:429–437
15.
Sheline YI, Barch DM, Price JL, et al: The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A 2009; 106:1942–1947
16.
Hamilton JP, Farmer M, Fogelman P, et al: Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience. Biol Psychiatry 2015; 78:224–230
17.
Sheline YI, Price JL, Yan Z, et al: Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci U S A 2010; 107:11020–11025
18.
Seeley WW: The salience network: a neural system for perceiving and responding to homeostatic demands. J Neurosci 2019; 39:9878–9882
19.
Sridharan D, Levitin DJ, Menon V: A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci U S A 2008; 105:12569–12574
20.
Rolls ET: Functions of the anterior insula in taste, autonomic, and related functions. Brain Cogn 2016; 110:4–19
21.
Uddin LQ: Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci 2015; 16:55–61
22.
Ongur D, Price JL: The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex 2000; 10:206–219
23.
Hamani C, Mayberg H, Stone S, et al: The subcallosal cingulate gyrus in the context of major depression. Biol Psychiatry 2011; 69:301–308
24.
Gudayol-Ferre E, Pero-Cebollero M, Gonzalez-Garrido AA, et al: Changes in brain connectivity related to the treatment of depression measured through fMRI: a systematic review. Front Hum Neurosci 2015; 9:582
25.
Li B, Liu L, Friston KJ, et al: A treatment-resistant default mode subnetwork in major depression. Biol Psychiatry 2013; 74:48–54
26.
Posner J, Hellerstein DJ, Gat I, et al: Antidepressants normalize the default mode network in patients with dysthymia. JAMA Psychiatry 2013; 70:373–382
27.
An J, Wang L, Li K, et al: Differential effects of antidepressant treatment on long-range and short-range functional connectivity strength in patients with major depressive disorder. Sci Rep 2017; 7:10214
28.
Wang L, Xia M, Li K, et al: The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp 2015; 36:768–778
29.
Dunlop BW, Binder EB, Cubells JF, et al: Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT): study protocol for a randomized controlled trial. Trials 2012; 13:106
30.
Dunlop BW, Kelley ME, Aponte-Rivera V, et al: Effects of patient preferences on outcomes in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study. Am J Psychiatry 2017; 174:546–556
31.
Hamilton M: A rating scale for depression. J Neurol Neurosurg Psychiatry 1960; 23:56–62
32.
First MB, Williams JB, Spitzer RL, et al: Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Clinical Trials Version (SCID-CT). New York, Biometrics Research, New York State Psychiatric Institute, 2007
33.
Beck AT, Rush AJ, Shaw BF, et al: Cognitive Therapy of Depression. New York, Guilford, 1979
34.
Dunlop BW, Rajendra JK, Craighead WE, et al: Functional connectivity of the subcallosal cingulate cortex and differential outcomes to treatment with cognitive-behavioral therapy or antidepressant medication for major depressive disorder. Am J Psychiatry 2017; 174:533–545
35.
Yeo BTT, Krienen FM, Sepulcre J, et al: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011; 106:1125–1165
36.
Fournier JC, DeRubeis RJ, Shelton RC, et al: Prediction of response to medication and cognitive therapy in the treatment of moderate to severe depression. J Consult Clin Psychol 2009; 77:775–787
37.
Zeharia N, Hertz U, Flash T, et al: New whole-body sensory-motor gradients revealed using phase-locked analysis and verified using multivoxel pattern analysis and functional connectivity. J Neurosci 2015; 35:2845–2859
38.
van den Heuvel MP, Sporns O: An anatomical substrate for integration among functional networks in human cortex. J Neurosci 2013; 33:14489–14500
39.
Dum RP, Levinthal DJ, Strick PL: The mind-body problem: circuits that link the cerebral cortex to the adrenal medulla. Proc Natl Acad Sci U S A 2019; 116:26321–26328
40.
Pariante CM, Lightman SL: The HPA axis in major depression: classical theories and new developments. Trends Neurosci 2008; 31:464–468
41.
Strick PL, Dum RP, Rathelot JA: The cortical motor areas and the emergence of motor skills: a neuroanatomical perspective. Annu Rev Neurosci 2021; 44:425–447
42.
Touroutoglou A, Andreano J, Dickerson BC, et al: The tenacious brain: how the anterior mid-cingulate contributes to achieving goals. Cortex 2020; 123:12–29
43.
Wang Y, Bernanke J, Peterson BS, et al: The association between antidepressant treatment and brain connectivity in two double-blind, placebo-controlled clinical trials: a treatment mechanism study. Lancet Psychiatry 2019; 6:667–674
44.
Li L, Su YA, Wu YK, et al: Eight-week antidepressant treatment reduces functional connectivity in first-episode drug-naïve patients with major depressive disorder. Hum Brain Mapp 2021; 42:2593–2605
45.
Korgaonkar MS, Goldstein-Piekarski AN, Fornito A, et al: Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder. Mol Psychiatry 2020; 25:1537–1549
46.
Pantazatos SP, Yttredahl A, Rubin-Falcone H, et al: Depression-related anterior cingulate prefrontal resting state connectivity normalizes following cognitive behavioral therapy. Eur Psychiatry 2020; 63:e37
47.
Baeken C, Duprat R, Wu GR, et al: Subgenual anterior cingulate-medial orbitofrontal functional connectivity in medication-resistant major depression: a neurobiological marker for accelerated intermittent theta burst stimulation treatment? Biol Psychiatry Cogn Neurosci Neuroimaging 2017; 2:556–565
48.
Morres ID, Hatzigeorgiadis A, Stathi A, et al: Aerobic exercise for adult patients with major depressive disorder in mental health services: a systematic review and meta-analysis. Depress Anxiety 2019; 36:39–53
49.
Williams LM: Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry 2016; 3:472–480
50.
Johnstone T, van Reekum CM, Urry HL, et al: Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. J Neurosci 2007; 27:8877–8884
51.
Pico-Perez M, Radua J, Steward T, et al: Emotion regulation in mood and anxiety disorders: a meta-analysis of fMRI cognitive reappraisal studies. Prog Neuropsychopharmacol Biol Psychiatry 2017; 79:96–104
52.
Craighead WE: Cognitive and behavior therapies for major and persistent depressive disorders, in The American Psychiatric Publishing Textbook of Mood Disorders, 2nd ed. Edited by Nemeroff CB, Schatzberg AF, Rasgon N, et al. Washington, DC, American Psychiatric Publishing, 2022
53.
LeMoult J, Gotlib IH: Depression: a cognitive perspective. Clin Psychol Rev 2019; 69:51–66
54.
Doucet GE, Janiri D, Howard R, et al: Transdiagnostic and disease-specific abnormalities in the default-mode network hubs in psychiatric disorders: a meta-analysis of resting-state functional imaging studies. Eur Psychiatry 2020; 63:e57
55.
Andreescu C, Tudorascu DL, Butters MA, et al: Resting state functional connectivity and treatment response in late-life depression. Psychiatry Res 2013; 214:313–321
56.
Karim HT, Andreescu C, Tudorascu D, et al: Intrinsic functional connectivity in late-life depression: trajectories over the course of pharmacotherapy in remitters and non-remitters. Mol Psychiatry 2017; 22:450–457
57.
Fu CHY, Costafreda SG, Sankar A, et al: Multimodal functional and structural neuroimaging investigation of major depressive disorder following treatment with duloxetine. BMC Psychiatry 2015; 15:82
58.
Pringle A, Browning M, Cowen PJ, et al: A cognitive neuropsychological model of antidepressant drug action. Prog Neuropsychopharmacol Biol Psychiatry 2011; 35:1586–1592
59.
Maya Vetencourt JF, Sale A, Viegi A, et al: The antidepressant fluoxetine restores plasticity in the adult visual cortex. Science 2008; 320:385–388
60.
Ruiz-Perera L, Muniz M, Vierci G, et al: Fluoxetine increases plasticity and modulates the proteomic profile in the adult mouse visual cortex. Sci Rep 2015; 5:12517
61.
Yrondi A, Péran P, Sauvaget A, et al: Structural-functional brain changes in depressed patients during and after electroconvulsive therapy. Acta Neuropsychiatr 2018; 30:17–28
62.
Philip NS, Barredo J, Aiken E, et al: Neuroimaging mechanisms of therapeutic transcranial magnetic stimulation for major depressive disorder. Biol Psychiatry Cogn Neurosci Neuroimaging 2018; 3:211–222
63.
Kotoula V, Webster T, Stone J, et al: Resting-state connectivity studies as a marker of the acute and delayed effects of subanaesthetic ketamine administration in healthy and depressed individuals: a systematic review. Brain Neurosci Adv 2021; 5:23982128211055426
64.
Ning L, Makris N, Camprodon JA, et al: Limits and reproducibility of resting-state functional MRI definition of DLPFC targets for neuromodulation. Brain Stimul 2019; 12:129–138
65.
Lynch CJ, Power JD, Scult MA, et al: Rapid precision functional mapping of individuals using multi-echo fMRI. Cell Rep 2020; 33:108540
Information & Authors
Information
Published In
History
Received: 18 July 2021
Revision received: 19 April 2022
Revision received: 13 June 2022
Accepted: 21 June 2022
Published online: 18 January 2023
Published in print: March 01, 2023
Keywords
Authors
Author Contributions
Drs. Craighead and Mayberg contributed equally to this work.
Competing Interests
Dr. Dunlop has received research support from Acadia, Aptinyx, Compass, NIMH, Otsuka, Sage, and Takeda and has served as a consultant for Cerebral Therapeutics, Greenwich Biosciences, Myriad Neuroscience, Otsuka, Sage, and Sophren Therapeutics. Dr. Choi has served as a consultant for Abbott Laboratories. Dr. Nemeroff has served as a consultant for AbbVie, Acadia Pharmaceuticals, Alfasigma, ANeuroTech (a division of Anima BV), Axsome, BioXcel Therapeutics, Clexio, Corcept Therapeutics, EcoR1, EMA Wellness, Engrail Therapeutics, GoodCap Pharmaceuticals, Intra-Cellular Therapies, Janssen Research and Development, Magstim, Navitor Pharmaceuticals, Neuritek, Ninnion Therapeutics, Pasithea Therapeutics, Sage, Senseye, Signant Health, Silo Pharma, SK Life Science, Sunovion Pharmaceuticals, and XW Pharma; he has served on scientific advisory boards for ANeuroTech, the Anxiety and Depression Association of America (ADAA), the Brain and Behavior Research Foundation, Heading Health, the Laureate Institute for Brain Research, Magnolia CNS, Pasithea Therapeutics, Sage, Signant Health, Skyland Trail, and TRUUST Neuroimaging and on the boards of directors for ADAA, Gratitude America, Lucy Scientific Discovery, and Xhale Smart; he holds stock in Antares, BI Gen Holdings, Corcept Therapeutics, EMA Wellness, Naki Health, Seattle Genetics, TRUUST Neuroimaging, and Xhale; and he is named on U.S. patents 6,375,990B1 and 7,148,027B2. Dr. Craighead has served as a consultant for the George West Mental Health Foundation, as a board member of Hugarheill ehf (an Icelandic company dedicated to the prevention of depression), and as a scientific advisory board member for AIM for Mental Health and ADAA; he is supported by the Mary and John Brock Foundation, the Pitts Foundation, and the Fuqua family foundations; and he receives book royalties from John Wiley. Dr. Mayberg has received consulting and intellectual property licensing fees from Abbott Neuromodulation. The other authors report no financial relationships with commercial interests.
Funding Information
Supported by NIH grants P50 MH077083, RO1 MH080880, UL1 RR025008, M01 RR0039, and K23 MH086690. Forest Laboratories and Elli Lilly donated the study medications (escitalopram and duloxetine, respectively).
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBLogin options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).