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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Psychiatry Res. Author manuscript; available in PMC 2012 June 30.
Published in final edited form as:
PMCID: PMC3152489

Rostral anterior cingulate cortex activity and early symptom improvement during treatment for major depressive disorder


In treatment trials for Major Depressive Disorder (MDD), early symptom improvement is predictive of eventual clinical response. Clinical response may also be predicted by elevated pretreatment theta (4-7 Hz) current density in the rostral anterior cingulate (rACC) and medial orbitofrontal cortex (mOFC). We investigated the relationship between pretreatment EEG and early improvement in predicting clinical outcome in 72 MDD subjects across three placebo-controlled treatment trials. Subjects were randomized to receive fluoxetine, venlafaxine, or placebo. Theta current density in the rACC and mOFC was computed with Low-Resolution Brain Electromagnetic Tomography (LORETA). An ANCOVA, examining week 8 Hamilton Depression Rating Scale (HamD) percent change, showed a significant effect of week-2-HamD-percent-change, and a significant three-way interaction of week-2-HamD-percent-change × Treatment × rACC. Medication subjects with robust early improvement showed almost no relationship between rACC theta current density and final clinical outcome. However, in subjects with little early improvement, rACC activity showed a strong relationship with clinical outcome. The model examining mOFC showed a trend in the three-way interaction. A combination of pretreatment rACC activity and early symptom improvement may be useful for predicting treatment response.

Keywords: Depression, EEG, LORETA, Placebo, Orbitofrontal, rostral anterior cingulate

1. Introduction

Major Depressive Disorder (MDD) is difficult to treat because there is no accepted method to predict which treatment will benefit a particular patient. The current trial-and-error strategy for medication selection (Fochtman and Gelenberg, 2005) eventually leads to response for the majority of patients, but patients may have less than a 30% chance of recovery with an initial trial of an antidepressant medication (Trivedi et al., 2006). Recovery from depression would be hastened by development of predictors that could be measured prior to treatment or early in the course of treatment (Leuchter et al., 2009b). Such predictors include pretreatment regional brain activity, as well as early improvement in symptoms (Leuchter et al., 2009b).

Prior reports, using various neuroimaging techniques, have shown that subjects who ultimately respond to antidepressant treatment typically have elevated pretreatment metabolic activity in the rACC and mOFC, as demonstrated with positron emission tomorgraphy (PET) (Mayberg et al., 1997; Saxena et al., 2003), or increased theta current density as measured using quantitative electroencephalography (QEEG) (Pizzagalli et al., 2001; Mulert et al., 2007; Korb et al., 2009). Theta current density studies have utilized Low-Resolution Brain Electromagnetic Tomography (LORETA), which is a widely used and well-validated QEEG source localization algorithm (Seeck et al., 1998; Worrell et al., 2000; Oakes et al., 2004). It is not surprising that responders differ from non-responders in both glucose metabolism and theta current density, given that rACC theta current density has been shown to correlate with rACC metabolism (Oakes et al., 2004).

In addition to the predictions of pretreatment brain activity, numerous reports also have demonstrated a strong relationship between eventual response and symptom improvement in the first two weeks of treatment (Szegedi et al., 2003; Katz et al., 2004; Papakostas et al., 2007; Stassen et al., 2007; Szegedi et al., 2009). Conversely, the absence of early improvement during pharmacotherapy is associated with poor eight week outcome (Nierenberg et al., 1995; Szegedi et al., 2009), although the lack of specificity of these changes limits the usefulness of this measure as a sole predictor (Leuchter et al., 2009b; Szegedi et al., 2009). Whereas distinct lines of research have identified brain function and early symptom improvement as predictors of response, the relationship between these predictors is unknown. Understanding whether early improvement in symptoms is linked to, or independent of, rACC or mOFC activity is important in determining how these predictors could potentially be employed in clinical practice.

While elevated baseline rACC and mOFC activity have been associated with greater likelihood of clinical response, the relationship between pretreatment rACC/mOFC activity and early improvement regarding ultimate clinical outcome has not been examined1. It is not known whether baseline theta current density predicts symptom improvement early or at other time points during eight weeks of treatment. Similarly, it is not known whether the relationship between baseline activity and eight-week outcome is dependent on (or independent of) early clinical improvement.

This study investigated relationships among early improvement, rACC and mOFC activity, and clinical outcome. We hypothesized that early improvement in symptoms would be predictive of final clinical outcomes. Exploratory analyses examined the relationship of pre-treatment theta current density to clinical improvement at various time points during the first eight weeks of treatment, and also examined the combination and interaction of early improvement and pretreatment theta current density in the prediction of final clinical outcomes.

2. Methods

2.1 Study design

We analyzed resting EEG data collected from three placebo-controlled treatment trials of MDD as described previously (Korb et al., 2009). The trials used identical inclusion and exclusion criteria, and included an initial week of single-blinded placebo treatment, followed by eight weeks of double-blinded treatment with placebo or medication. In the medication arm, two of the trials used venlafaxine (150 mg. daily), and the third used fluoxetine (20 mg. daily). Data were analyzed for all 72 subjects who completed the eight weeks of treatment (fluoxetine n=13, venlafaxine n=24, placebo n=35).

2.2 Subjects

The UCLA Institutional Review Board approved all experimental procedures. Experimental procedures were explained fully to each subject, and written informed consent was obtained. Subjects were recruited from outpatient clinics of the UCLA Neuropsychiatric Hospital and through community advertisements. At entry, all subjects had 17-item Hamilton Depression Rating Scale (HamD) scores ≥ 16 (Hamilton, 1960) and met DSM-IV criteria for a major depressive episode diagnosed using the Structured Clinical Interview for DSM-IV (First et al., 1995). Demographics of the sample are shown in Table 1. Subjects were excluded if they suffered from any medical illness or received any medication known to significantly affect brain function, if they had a history of suicide attempt, or if they had previously failed to benefit from treatment with the antidepressant being studied. All subjects were free of any psychotropic medication for at least two weeks prior to enrollment.

Table 1
Subject Characteristics

2.3 EEG procedures

EEG recordings methods have been described previously (Cook et al., 2002), and utilized an extended International 10-20 System with an electrode cap of 36 tin recording electrodes (ElectroCap, Inc.; Eaton, OH) referenced to Pz. Electrode impedances were balanced and under 5kΩ for all channels. While subjects rested in the eyes-closed, maximally alert state in a sound-attenuated room with subdued lighting, up to twenty minutes of EEG data were collected using the QND system (Neurodata, Inc.; Pasadena, CA). Patients were frequently and vigorously re-alerted at the first sign of drowsiness. EEG data were digitized on line at 256 samples/channel/sec with a high-frequency filter of 70 Hz and a low-frequency filter of 0.3 Hz, as well as a notch filter at 60 Hz. Only EEG that was free of eye movement, drowsiness, excess muscle, or other artifacts was considered for further processing; an EEG technologist selected for processing the first 10 to 16 two-second epochs of data that met this criteria. Twenty to thirty-two seconds of EEG has been shown to be sufficient for characterizing clinically relevant differences in depression (Cook et al., 2002; Mientus et al., 2002; Bares et al., 2007; Korb et al., 2008). Prior to processing, a second EEG technologist, blinded to subject identity and treatment condition, independently confirmed that only artifact-free data were selected. The average number of epochs selected per subject was 14.1 ± 2.2. These epochs were then transformed to three-dimensional current density maps using LORETA.

2.4 LORETA analysis

The LORETA-KEY package ( (Pascual-Marqui et al., 1999) was used to compute theta-band (4-7 Hz) current density for each subject. Our LORETA methods have been described previously (Korb et al., 2009) and are summarized here. A cross-spectral matrix was used to compute current density in each of 2394 cortical voxels. For each subject, theta band current density was normalized to a total power of 1.0.

Based on previous studies, regions of interest (ROIs) were created for the rACC and mOFC (Fig 1A and 1B). The rACC ROI consisted of 14 voxels and was taken from the results of a previous study on current density and treatment response (Pizzagalli et al., 2001) (Fig. 1A). The mOFC ROI consisted of 34 voxels and was based on anatomical classifications (Fig. 1B). Voxels in each ROI were averaged and log-transformed, resulting in a single current density value for that ROI.

Figure 1
Regions of Interest

2.5 Statistical analysis

To examine the effect of week 2 HamD percent change on week 8 HamD percent change, a simple linear regression was run for both the placebo-treated group and the medication-treated group.

To examine the effect of pretreatment theta current density on HamD percent change at all available time points, we conducted a multilevel mixed-effects regression analysis. The multilevel regression included the factors of Visit (5 levels: 48 hours, Week 1, Week 2, Week 4 and Week 8) and Treatment (2 levels: medication and placebo). It also included baseline-theta-current-density as a continuous variable. In order to avoid co-linearity, baseline-theta-current-density was centered by subtracting the mean. All 2-way interactions and the 3-way interaction were included. A separate regression was run for each ROI (rACC, mOFC).

To predict final clinical outcome (week 8 HamD percent change) using a combination of baseline rACC theta current density (continuous) and early improvement (continuous), we applied an ANCOVA model. The ANCOVA included variables for week-2-HamD-percent-change (continuous) and baseline-theta-current-density (continuous) as covariates of interest, as well as a factor of Treatment (2 levels). The continuous variables were centered to avoid co-linearity. The ANCOVA also included all 2-way interactions and the 3-way interaction. A separate ANCOVA was conducted for each ROI.

3. Results

3.1 Week 2 HamD percent change

At week 2 the medication group (n = 37) showed a mean percent change in HamD of 35.6% (S.D. 27.1%). The placebo group (n = 35) showed a mean week 2 HamD percent change of 31.1% (S.D. 27.2%). Percent change in HamD at week 2 was a highly significant predictor of week 8 HamD percent change in both the medication group (β = 0.711, P <0.001) and the placebo group (β = 0.605, P = 0.004).

3.2 Theta current density and HamD percent change at multiple time points

The multilevel mixed-effects regression showed a significant effect of baseline rACC theta current density by Visit (χ25 = 11.30, P = 0.046). The marginal effects of rACC by visit are listed in Table 2. rACC was significantly related to HamD percent change only at week 2 and week 8. At both time points, elevated baseline rACC theta current density was associated with greater decrease in symptoms. There was no significant difference by treatment group on the effect of rACC theta current density by Visit (χ24 = 2.81, P = 0.590).

Table 2
Marginal Effects of Theta Current Density by Visit

In the mOFC region, the multilevel mixed-effects regression the effect of baseline theta current density by Visit approached significance (χ25 = 10.70, P = 0.058). For comparison to rACC the marginal effects of mOFC by visit are listed in Table 2, even though the interaction by Visit was not significant. There was no significant difference by treatment group on the effect of mOFC theta current density by visit (χ24 = 3.91, P = 0.418).

3.3 Combining early improvement and baseline theta current density

The full results of the ANCOVA are shown in Table 3. In ANCOVA including the rACC, the overall model was significant (F7, 64 = 5.64, P < 0.001), and explained 38.2% of the variance in week 8 HamD percent change. There was a highly significant main effect of week-2-HamD-percent-change on week-8-HamD-percent-change (F1, 64 = 18.18, P < 0.001). The factor Treatment also had a significant effect (F1, 64 = 5.05, P = 0.028). The three-way interaction of week-2-HamD-percent-change × Treatment × rACC was also significant (F1, 64 = 4.24, P = 0.044).

Table 3

Figure 2 illustrates the relationship, by treatment, among baseline rACC theta current density, early improvement, and percent change in HamD at eight weeks. Medication subjects with robust early improvement showed almost no relationship between rACC theta current density and final clinical outcome. However, subjects with little to no early improvement showed a strong relationship between theta current density and final clinical outcome. In placebo-treated subjects, rACC theta current density and week-8-HamD-percent-change showed a different relationship. Placebo subjects with robust early improvement show a slight positive relationship between rACC theta current density and week-8-HamD-percent-change. However, placebo subjects with little to no early improvement show a slight negative relationship between rACC theta current density and week-8-HamD-percent-change.

Figure 2
Pretreatment rACC Theta Current Density and Week Eight HamD Percent Change by Early Improvement

In the ANCOVA including the mOFC, the overall model was significant (F7, 64 = 5.64, P < 0.001), and explained 37.7% of the variance in week-8-HamD-percent-change. There was also a highly significant main effect of week-2-HamD-percent-change on week-8-HamD-percent-change (F1, 64 = 16.10, P < 0.001). The factor of Treatment was significant (F1, 64 = 4.82, P = 0.032). The three-way interaction of week-2-HamD-percent-change × Treatment × mOFC showed a similar overall pattern as seen in the rACC but did not reach significance (F1, 64 = 3.22, P = 0.077).

4. Discussion

As hypothesized, the present study showed that across treatments early improvement in symptoms was significantly correlated with final clinical outcome, agreeing with numerous previous reports (Szegedi et al., 2003; Katz et al., 2004; Papakostas et al., 2007; Stassen et al., 2007; Szegedi et al., 2009). Further results demonstrated, for the first time, relationships among early improvement in depressive symptoms, pretreatment rACC/mOFC activity, and clinical outcome. Higher pretreatment rACC theta current density was associated with greater symptom improvement at both week 2 and week 8. A similar trend was seen in the mOFC. Finally, combining early improvement and baseline rACC theta current density in a single analysis revealed a significant interaction of these variables in predicting week 8 outcomes. The interaction showed a differential effect of rACC/mOFC theta current density depending on type of treatment and the degree of early improvement.

The present study is the first to show a relationship between pre-treatment rACC theta current density and medication or placebo response over time intervals shorter than four weeks. In prior work, pretreatment activity in these same regions has been evaluated in relationship to acute response to sleep deprivation. Whereas we did not see a significant relationship over our shortest time interval (48 hours), previous reports have found elevated pretreatment rACC and mOFC perfusion and metabolism in next-day responders to sleep deprivation (Volk et al., 1997; Wu et al., 1999). Differences between our results and those from sleep deprivation studies may be due to differences in the mechanisms underlying these interventions.

The current finding that higher pretreatment rACC activity is related to greater degree of response at week 8 endpoint is consistent with an independent LORETA report, which found that higher pretreatment rACC theta current density was associated with response following six months of open label antidepressant treatment (Pizzagalli et al., 2001). Prior studies utilizing PET have reported mixed results regarding the direction of the relationship between pretreatment rACC activity and response. Whereas Mayberg and colleagues (Mayberg et al., 1997) and Saxena and co-workers (Saxena et al., 2003) found higher pretreatment rACC metabolism associated with medication response after six to twelve weeks of treatment, respectively, groups led by Brody (Brody et al., 1999) and Konarski (Konarski et al., 2009) showed lower pretreatment rACC metabolism in responders. Given the interaction seen in our results, it is possible that discrepancies among the PET study findings may be explained by differences in subjects' early improvement across studies. Alternatively, since neither of these studies was placebo controlled, placebo response may play a role.

In contrast to our present findings, Mulert and colleagues showed that pretreatment rACC and mOFC theta current density differed between responders and non-responders in a four-week open label study (Mulert et al., 2007). Our analysis did not show a significant relationship between rACC or mOFC theta current density and degree of response at four weeks. Methodological differences may explain the discrepancies. For example, the Mulert study was conducted on an inpatient population with open-label treatment. Perhaps more puzzling, however, is the observation from our data that that rACC and mOFC theta current density related significantly to HamD percent change at week 2 and week 8 but not week 4. This may be explained by research from our lab that has identified, using growth mixture modeling in this same subject population, a class of patients who show symptom volatility in response to medication (Hunter et al., 2010). Patients in the symptom volatility group show little or no early improvement, large improvements around week 4, but ultimately do not respond to medication at later timepoints. Thus the current result may be explained by the fact that these subjects are clinically better off at 4 weeks than they are at 2 or 8 weeks.

Our results illustrate a complex relationship among treatment, theta current density, early improvement, and eight week outcome (Figure 2). Most notably, there was a significant interaction of rACC × Week 2 HamD Percent Change × Treatment. Thus the effect of rACC activity on final clinical outcome depended both on how much subjects improved at week 2 and whether they were treated with medication or placebo. Medication subjects with robust early improvement showed almost no relationship between rACC activity and week 8 HamD percent change. However, subjects who showed little or no early improvement had a strong positive relationship between these two variables. These relationships are further illustrated in figure 3 with individual subject data. This complex relationship of rACC theta current density and week 8 outcome may have clinical relevance for patients who fail to improve early in treatment; if their pretreatment rACC activity is known, then their likelihood of subsequent response may, in part, be determined.

Figure 3
Week 2 response and Wk8HamPctChg by rACC Theta (centered)

Placebo-treated subjects showed a different relationship from medication treated subjects among theta current density, early improvement and eight week HamD percent change. In placebo subjects with robust early improvement, higher baseline theta current density was associated with better outcome at eight weeks. However, in placebo subjects with little or no early improvement, higher theta was associated with worse outcomes. There is scant relevant literature to assist in interpreting this finding. Despite the relevance to clinical psychiatry, the mechanisms of the placebo antidepressant response remain incompletely understood. Prior work by Mayberg and colleagues (Mayberg et al., 2002) reported changes in subgenual ACC in placebo responders, an area which has extensive connections with both rACC and mOFC. While neither rACC nor mOFC have been directly implicated in the placebo antidepressant response, they have been shown to play a role in other types of placebo response. For example, research on pain and placebo analgesia has shown that higher activity in both OFC and rACC are related to a stronger placebo response (Kong et al., 2006).

Current research cannot fully explain the variability in time-course and degree to which MDD patients respond to treatment. Therefore, the mechanism of how rACC activity relates to response is also unclear. However, there is evidence that rACC activity may influence or be influenced by the same mechanisms that lead to treatment response. For example, two common targets of antidepressant medications, the serotonin transporter and monamine oxidase, exist in high concentrations in the rACC in depressed subjects (Varnas et al., 2004; Meyer et al., 2009). In addition, the rACC may also be influenced by the mechanisms that contribute to depression; for example, polymorphisms in genes that control these targets may lead both to increased risk for depression (Hariri et al., 2005; Pezawas et al., 2005; Aklillu et al., 2009; Rivera et al., 2009; Fan et al., 2010; Holmes et al., 2010) as well as to functional and structural abnormalities in the rACC (Pezawas et al., 2005; Passamonti et al., 2008; Holmes et al., 2010).

We interpret elevated rACC theta current density as evidence of elevated neural activity, because rACC theta current density has been shown to correlate positively with rACC glucose metabolism (Pizzagalli et al., 2003). In the mOFC, however, no significant correlation has been demonstrated between theta current density and glucose metabolism (Pizzagalli et al., 2003). Nonetheless, mOFC theta current density may be functionally related to rACC theta current density, as these areas have been shown to be physically and functionally connected (Ongur and Price, 2000; Kringelbach and Rolls, 2004). The rACC and mOFC are both important areas in the limbic-cortical dysregulation model of MDD (Mayberg, 2003). The rACC is thought primarily to process emotion and attention (Lane et al., 1998), while the mOFC primarily processes emotion and reward (O'Doherty et al., 2001; Rolls et al., 2008). Elevated activity in these areas may increase the salience and rewarding aspects of treatment. It has been proposed that this elevated activity may reflect a compensatory neural response to depression that renders a patient more likely to improve in response to treatment with medication (Mayberg et al., 1997; Pizzagalli et al., 2001; Saxena et al., 2003) and sleep deprivation (Volk et al., 1997; Wu et al., 1999). Our results are consistent with this model.

This study has several limitations. First, it should be noted that the LORETA method is not a direct measure of rACC or mOFC activity, but rather a low-resolution estimate of current density. Second, we do not have information to allow for clinical subtyping of depression (e.g., melancholic depression), and it is possible that such subtypes may demonstrate different neurophysiologic characteristics. In addition, there are limitations on applying the results of this study to clinical practice. While rACC activity measures may one day be employed clinically, the research is not yet sophisticated enough to predict individual responses.

These findings may be viewed within the framework of recent results indicating that prefrontal QEEG monitoring may be particularly useful in the management of MDD (Cook et al., 2009; Leuchter et al., 2009a; Leuchter et al., 2009c; Hunter et al., 2010). For example, early changes in the theta band in the QEEG measure cordance may differentiate between eventual responders, non-responders and patients exhibiting symptom volatility (Hunter et al., 2010). Similarly the QEEG Antidepressant Treatment Response (ATR) index, which combines baseline and week 1 change measurements, may predict remission on medication (Leuchter et al., 2009a; Leuchter et al., 2009c).

Results of this study add to our understanding of the factors that contribute to early improvement, as well as to the relationships between pretreatment brain function and early improvement in predicting clinical outcome. Early improvement is clinically relevant not only because expedited symptom improvement is desirable, but also because prior work has shown that symptom improvement in the first two weeks is predictive of subsequent response (Szegedi et al., 2003; Katz et al., 2004; Papakostas et al., 2007; Stassen et al., 2007). Further study of the relationships between brain function and early improvement may help point to mechanisms of clinical response in MDD, and improve prediction of treatment outcomes.


The authors thank Barbara Siegman R.EEG.T., and Suzie Hodgkin, R.EEG.T., (recording EEG data); Michelle Abrams, R.N., (subject recruitment and evaluation); David Schairer (EEG data processing); and UCLA Academic Technology Services (ATS) Statistical Consulting. This work was supported by NIMH-NRSA training fellowship (NIMH T32 MH17140), by a grant from the National Center for Complementary and Alternative Medicine (R01 AT002479), and by an endowment in depression research from Joanne and George Miller and Family. We also acknowledge the grant support of Eli Lilly and Company, Wyeth-Ayerst Laboratories, and Aspect Medical Systems, Inc. LORETA-KEY software was provided by Roberto D. Pascual-Marqui, KEY Institute for Brain-Mind Research, University of Zurich.


1Our lab has previously examined response and baseline theta current density in these subjects (Korb et al., 2009). We have also examined data from these subjects to address other research questions (Cook et al., 2002; Leuchter et al., 2002; Leuchter et al., 2004; Hunter et al., 2006; Korb et al., 2008; Cook et al., 2009; Hunter et al., 2010).

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