This study found three distinct patterns of change in symptom severity over eight weeks of antidepressant treatment for which there was some evidence of neurophysiologic differences. GMM analyses identified ‘responder,’ ‘non-responder,’ and ‘symptom volatility’ trajectory shapes estimated to comprise 62%, 21%, and 17% of subjects, respectively. The responder and non-responder patterns are consistent with conceptualizations of monotonic improvement over the course of antidepressant treatment with greater effectiveness for some patients (responders) and lesser effectiveness for other (non-responders). In contrast, the symptom volatility trajectory suggests that a small subgroup of patients exhibits a highly fluctuating course symptom severity with alternating periods of improvement and worsening. Changes in the QEEG marker, MRF cordance, were different across 48-h and 1-week timepoints as a function of symptom trajectory class among subjects randomized to antidepressant medication.
Interestingly, although non-responders were shown to have significantly greater illness severity at baseline, there was no difference in baseline Ham-D17 scores between responder and symptom volatility groups. Thus, at the outset of treatment, subjects who would later show stable steady improvement versus an erratic symptom course were indistinguishable in terms of overall symptom severity as measured the Ham-D17 total score.
The symptom volatility group may be of special clinical interest. First, this subgroup may represent patients whose depression is especially difficult to treat and/or monitor. Prior reports have suggested that periods of clinical worsening in the early stages of antidepressant treatment may portend poorer long-term outcome (Cusin et al., 2007
; Perahia et al., 2008
). Moreover, subjects who exhibit early worsening are at greater risk for discontinuation of treatment (Beasley et al., 2000
; Chelben et al., 2001
; Kaplan, 1997
). Second, the cause of symptom volatility in this subgroup is unknown. One possibility is that the fluctuating symptoms represent an unstable placebo-like response. This idea would be in keeping with conceptions put forth by Quitkin and colleagues that early improvement followed by worsening (i.e., an inverted U pattern of response) reflects placebo effects rather than “true drug” effects (Quitkin et al., 1991
). However, the pattern found here is polyphasic, and, in the present study, placebo subjects did not exhibit a similarly volatile pattern. Third, it is possible that this symptom pattern is detecting heterogeneity in depressive illness. It is curious that the rapid fluctuation of symptom changes in this group is similar to the reported reaction of patients with bipolar disorder (BPD) to antidepressant treatment, namely fluctuation of symptoms and induction of “cycling.” Although we have no direct evidence, one could speculate that some subjects diagnosed with MDD actually were suffering from a bipolar-spectrum illness (Akiskal, 1993
), such as BPD type II, which is characterized primarily by depressive episodes. For these individuals, antidepressant treatment could possibly induce rapidly oscillating symptoms (American Psychiatric Association, 2002
; Sachs et al., 2000
The observation that there may be a subgroup with unstable response also has implications for research on markers of antidepressant ‘response/non-response’ or ‘remission/non-remission’ when those classifications are derived using endpoint outcomes. Whether outcome is predicted by QEEG or other imaging markers, genetic markers, or clinical and demographic characteristics, the ability to accurately predict a dichotomous outcome partly depends upon the stability of that outcome measure. A ‘symptom volatility’ pattern is clearly an unfavorable clinical outcome yet use of a single endpoint criterion leaves the dichotomous responder/non-responder classification of symptomatically unstable subjects to chance. Thus any finding based upon unstable symptom change outcomes introduces a source of variance into the outcome of clinical trials that may be difficult to detect. If an estimated 17% of subjects were to fall into this category, it could introduce a sizeable margin of error into any clinical trial. Unless subjects with unstable symptom changes are identified as non-responders, their inclusion in endpoint analyses may obscure ability to test both the effectiveness of treatment and the predictive capability of a bio-marker for response.
The MRF cordance biomarker, previously associated with antidepressant effects and endpoint response or remission, was found to differ between medication subjects classified according to responder versus non-responder outcome trajectories. Consistent with prior observations showing an association between decreases in MRF theta cordance within the first week of antidepressant treatment, and end-of-trial clinical improvement in MDD (Cook et al, in press
; Leuchter et al., 2005
), the present study found a significantly greater week 1 decrease in MRF cordance in GMM responders as compared to non-responders. This finding provides support for a neurophysiologic basis underlying these symptom trajectory patterns. Subjects in the symptom volatility group showed an intermediate change in MRF cordance at week 1 that did not statistically separate from responders. One suggestion is that the cordance marker might simply act as a marker of clinical severity, or, of improvement regardless of the durability of response or the means by which it is attained. However, contrary to this interpretation, there was no significant association between the MRF marker and placebo response trajectories. Results of our analyses indicated that GMM outcome class differences in MRF cordance changes were moderated by time indicating that the assessment timepoint is important. Future studies should examine different timepoints and/or brain regions that could potentially help demarcate unique neurophysiologic features of subjects who express symptom volatility.
To our knowledge, this is the first report to relate symptom trajectory outcomes during treatment for MDD to a neurophysiologic marker. Findings should be interpreted within the limits of this study. First, this GMM approach should be replicated in an independent sample of subjects. Like any model, GMM is based on a set of assumptions (e.g., the assumption of normality within each latent class) that may not be completely fulfilled in any given data set. Second, the cause of any of the symptom trajectory patterns, including the symptom volatility pattern, cannot be determined from the present study. Response patterns in the various outcome classes may or may not be related to medication effects in varying degrees. For example, it is unknown to what extent fluctuating symptoms may be due to the natural course of illness and/or effects of treatment (e.g., medication side effects). Further, patients may have improvement in some symptoms while experiencing worsening of other symptoms; additional studies are needed to address changes in specific symptoms of interest. Third, subjects were treated with fluoxetine or venlafaxine; larger trials with random assignment to various antidepressants would be needed to assess potential effects of specific medications. Fourth, subjects in this study were not distinguished in terms of depressive subtypes (e.g., atypical depression) or comorbid anxiety disorders—clinical factors that can affect symptom ratings and SSRI response (Fava et al., 2008
). Future studies of better-characterized subjects should be conducted to examine such clinical characteristics as potential moderators of response trajectory. Last, we examined QEEG cordance changes only at a single region and at a single timepoint previously shown to be related to neurophysiological or clinical effects of medication; it is possible that other groups of electrodes, other timepoints, or other EEG parameters might differentiate subjects who express a volatile symptom change outcome pattern.