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Can We Predict Response to Antidepressants?: Page 2 of 2

Can We Predict Response to Antidepressants?: Page 2 of 2

The genetics of SERT have been studied at length as a predictor of treatment response in MDD. Several groups have found that depressed patients with a certain genetic variant of SERT respond more favorably to SSRIs,19 although this finding has not been replicated in all studies.19, 20 This genetic variant may be independent of SERT binding,21 so that to the extent that each of these measures is predictive of outcome individually, they may have greater predictive power when taken together.

Therefore, it seems that patients who have elevated 5-HT1A receptor binding and decreased SERT binding are less likely to achieve remission with antidepressant treatment, whereas patients whose 5-HT1A receptor and SERT binding are more like those of healthy controls are more likely to achieve remission with treatment. This profile of elevated 5-HT1A binding and decreased SERT binding associated with worse outcomes could be at least partially explained by the genetic variant of the 5-HT1A receptor that we described previously. A genetic variant of SERT could contribute independently to the likelihood of remission. A model of how this profile could arise is presented in Figure 1.

Other genetic markers

Several other genes have been examined in pharmacogenetic studies as possible predictors of treatment outcome in MDD. These include polymorphisms in genes that affect drug metabolism, such as cytochrome P-450 2D6; downstream secondary messenger systems in neurons, such as G-proteins; the viability of neurons, such as brain-derived neuro-trophic factor; and neurotransmitter regulation, such as tryptophan hydroxylase 1.22 Whole-genome association studies may be used in the future to compare SNPs across the entire human genome between remitters and nonremitters in response to specific antidepressant treatments.23


PET can be used to measure the relative rate of glucose metabolism, an indirect measure of resting brain activity, with the use of the glucose radioligand [18F]fluorodeoxyglucose (FDG). Regional glucose metabolism has been studied as a predictor of outcome with antidepressant treatment.24 Depressed patients underwent FDG-PET, followed by 6 weeks of medication-based treatment. Glucose metabolism was compared between responders and nonresponders. Those who responded had higher resting glucose metabolism in the rostral anterior cingulate cortex than patients who did not respond. The authors described connections between the rostral anterior cingulate cortex and several other regions in the brain that could be involved in a poorly functioning circuit related to depression. This finding has been replicated in subsequent PET studies,25 indicating the possible importance of the rostral anterior cingulate cortex in the response to antidepressant medications. Another FDG-PET study found the opposite relationship between metabolism and treatment response in the left ventral anterior cingulate cortex, an adjacent brain region.26


A series of fMRI studies have looked at brain activations in response to specific probes as predictors of response to treatment in patients with MDD. In one of the first of these studies, patients viewed pictures of emotionally neutral or charged scenes, and their patterns of brain activation were used to predict subsequent response to the antidepressant venlafaxine.27 The investigators found that larger responses in the anterior cingulate cortex to presentation of emotionally negative pictures were associated with subsequent improvement with venlafaxine treatment, which is consistent with the FDG-PET studies previously described. This finding was recently replicated using responses to emotional faces to predict outcome with fluoxetine treatment.28 Interestingly, that study also analyzed structural MRI scans on the same patients, and found increased gray matter in the anterior cingulate cortex and other regions among fluoxetine responders, suggesting that the changes in brain activity that predict treatment response may reflect underlying differences in brain structure.

A more recent fMRI study looked at brain activation in response to the presentation of emotionally charged words as a predictor of recovery from MDD following CBT.29 It found a specific pattern of brain activity during the viewing of emotionally negative words that was associated with subsequent improvement with CBT: low activation of the subgenual cingulate cortex and high activation of the amygdala. The amygdala is involved in processing emotional stimuli, and its responses to emotional stimuli may be controlled by connections from the subgenual cingulate cortex.30 This finding involving the subgenual cingulate cortex contradicts the 2 fMRI studies involving medication treatment described above. Whether this is because of methodological differences between the studies or, more promisingly, caused by different patterns of cingulate activity predicting differential response to medication versus CBT remains an important question.


From this brief review, we hope to convey a sense of the breadth of approaches that show promise in predicting response to treatments of depression, as well as some of their limitations, including nonstandardized methodology for conducting functional brain imaging scans and conflicting results between studies. The measures we have described in this review are only partially predictive of treatment outcome when used individually.

To address this, we are currently developing statistical models that combine relevant clinical, brain imaging, and genetic data to predict which treatments will be the most effective for a particular patient. Whereas brain imaging studies to date have examined activity or chemistry in relatively large regions of the brain as predictors of treatment outcome, we are using measures of brain activity or chemistry in areas as small as a few cubic millimeters in these models, allowing us to rely less on preconceived notions about the anatomy and organization of the brain.31 In addition to predicting treatment outcome, these models may be used for predicting diagnosis (such as unipolar vs bipolar depression), as well as risk of suicidal behavior. Finally, studies are needed in which multiple biological and clinical measures are obtained before randomization to different standardized treatment conditions in order to bring these techniques closer to being ready for clinical use.



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