MDD remains a leading cause of disability worldwide and is associated with profound personal suffering and staggering costs to society (Greenberg et al, 2003
; Holma et al, 2010
; Lopez et al, 2006
). Despite an array of antidepressant options and psychotherapies, there is currently no empirically validated approach to selection of treatment that is based on an individual's likelihood of response to a given therapy. As a result, treatment in clinical practice follows a trial-and-error approach. The goals of the current review were: (1) to perform a meta-analysis to evaluate the promise of resting rACC activity as a biomarker of treatment response in depression; (2) to advance hypotheses concerning psychological and neurobiological mechanisms that might explain this link; and (3) to integrate these findings and hypotheses within the larger literature implicating frontocingulate dysfunction in depression.
Results from the current meta-analysis indicate that increased pre-treatment rACC activity is a reliable marker of treatment response in depression, which is associated with a large effect size (Cohen's d value: 0.918) and has been replicated across 19 studies. Highlighting the robustness of this finding, a link between increased rACC activity and positive antidepressant response has emerged across treatments, including various classes of antidepressant drugs (eg, SSRIs, atypical antidepressants, ketamine), sleep deprivation, and rTMS. The clinical and research implications of these findings are substantial. From a clinical perspective, the current meta-analysis indicates that it is possible to identify a priori individuals with a low probability of response to monotherapy, who might benefit from a combination of treatment interventions at the outset. From a research perspective, the findings contribute to a better understanding of mechanisms associated with treatment response. In this context, through an integration of neuropsychological, electrophysiological, and neuroimaging data, I proposed that elevated resting rACC activity may foster better treatment outcome through (1) adaptive forms of reflective, self-focused processing, and (2) adaptive modulations between the DN and TPN, including ability to suppress rACC activation in situations requiring inhibition of emotional responding and recruitment of cognitive control. Future studies will be required to test the hypothesis that low resting rACC activity is associated with deficits in deactivating this DN hub and bringing online the TPN during cognitive tasks, which might foster the emergence of maladaptive emotional and self-focused processing, leading to treatment nonresponse. Along similar lines, the possibility that reduced task-induced rACC deactivation during cognitive and affective challenges represents a floor effect due to low resting rACC activity needs to be carefully investigated.
Although reduced ability to suppress rACC activation during emotional and cognitively demanding tasks has emerged as one of the most consistent findings in the functional neuroimaging literature in depression, several outstanding questions remain. First, because this finding has emerged almost exclusively from currently depressed samples, it is currently unknown whether reduced task-induced rACC deactivation is a correlate of depression or rather a vulnerability factor. Initial findings of potentiated rACC responses to errors during an executive task in psychiatrically healthy individuals carrying genetic variants linked to increased risk for depression (Holmes et al, 2010
) are consistent with the view of a putative vulnerability factor; these findings await replication and studies in additional at-risk samples (eg, unaffected offspring of individuals with depression) will be required for conclusive tests of this hypothesis.
Second, the neurobiological underpinnings of rACC dysfunction remain poorly understood. Initial evidence suggests that reduced task-induced rACC deactivation in MDD might be linked to altered glutamatergic metabolism. Specifically, in a combined fMRI-spectroscopy study, Walter et al (2009
) reported that in unmedicated MDD subjects, task-related rACC deactivation during an emotional judgment task correlated with levels of glutamate, the main excitatory neurotransmitter, in the rACC. These findings contrast with data in controls indicating that resting rACC levels of the main inhibitory neutransmitter, γ
-aminobutyric acid (GABA), correlated positively with rACC deactivation during an emotional task (Northoff et al, 2007
; Walter et al, 2009
). Together, these findings raise the possibility that resting state activity in MDD might shift from a mainly GABA- to glutamate-meditated modulation. In light of an emerging role of glutamate in the pathophysiology of MDD and the mechanism of action of antidepressants (Hashimoto, 2009
; Sanacora et al, 2008
), future studies are needed to understand the significance of these findings and explore the relevance of these neurochemical abnormalities to treatment response and biased emotional processing in depression.
Third, studies investigating cognitive behavioral therapy have not observed links between rACC activity and treatment outcome (Siegle et al, 2006
; see also Konarski et al 2009
). The reasons for these discrepancies are not immediately clear, particularly considering that strategies targeting maladaptive cognition and rumination are important components of CBT. In light of the hypothesis that increased resting rACC activity fosters treatment outcome by supporting adaptive forms of rumination, including a mindful, nonevaluative focus, it will be important to test whether this biomarker predicts outcome to mindfulness-based cognitive therapy, which is an effective treatment for preventing depressive relapse and recurrence (eg, Godfrin and van Heeringen, 2010
; Kuyken et al, 2008
; Ma and Teasdale, 2004
Finally, in spite of the robust link between rACC activity and antidepressant response, there are three important weaknesses in this literature that should be addressed. First, with a few exceptions (eg, Korb et al, 2009
; Mayberg et al, 2000
), most studies have used naturalistic designs without placebo control, likely because of several practical and ethical considerations (eg, expenses of fMRI/PET scans and possible concerns about exposing placebo-treated patients to radioactive PET tracers). Nevertheless, as placebo effects appear in 25–60% of treated individuals (Quitkin, 1999
), ‘active' drug responders may in fact be placebo responders. This makes it difficult to differentiate between specific and nonspecific components of treatment response. The report from Korb et al (2009)
that increased resting rACC activity predicted antidepressant—but not placebo—response is encouraging, but additional placebo-controlled studies are warranted. In light of findings emerging from the current meta-analysis, EEG methodologies might be especially useful to circumvent some of the practical and ethical considerations mentioned above.
Second, few studies have compared responders with both nonresponders and
healthy controls (eg, Pizzagalli et al, 2001
), and the author is not aware of any study that has contrasted treatment responders to both
nonresponders and healthy controls both
before and after treatment in a placebo-controlled design. These contrasts are essential for identifying predictors vs
mediators of outcome, as well as for pinpointing the effects of antidepressants on the neural mechanisms under investigation. Third, all studies reviewed have implemented simple univariate statistical models and have not integrated measures of rACC activity with other biological, clinical, genetic, or behavioral data. The development of more advanced, multivariate statistical algorithms might improve our ability to predict outcome on an individual basis, which remains a critical priority for the field (Insel, 2009