Predicting Treatment Response in Obsessive-Compulsive Disorder

An obvious hypothesis, given the extensive range of symptoms that may be present in OCD, is that the predominant symptoms displayed can be used to group patients into treatment categories. Most studies have approached this by categorizing patients into subgroups based on presence/absence of specific OCD symptoms, such as compulsions or cleaning rituals. This approach has not been particularly successful (see reviews^8,9). A more promising approach applies factor analysis techniques to identify symptoms that are highly intercorrelated, thus collapsing many symptoms into a few symptom dimensions.^8–10 In the initial study, the many items in the Yale-Brown Obsessive Compulsive Scale symptom checklist were subsumed into three major dimensions: aggressive/sexual/religious; contamination; and symmetry/hoarding.¹⁰ The symmetry/hoarding dimension correlated with comorbid Tourette's syndrome or chronic tic disorder, suggesting that this subgroup would benefit from neuroleptic medication. In the larger follow-up study, the number of dimensions was expanded to five: symmetry/ordering; hoarding; contamination/cleaning; aggressive/checking; and sexual/religious obsessions.⁹ Not surprisingly, overall severity of symptoms prior to treatment was related to post-treatment severity of symptoms. In addition, higher scores on the hoarding dimension correlated with poorer response to SSRIs.

Both quantitative electroencephalography (qEEG) and event-related potential (ERP) studies have been done in the context of predicting treatment response in OCD.

In qEEG analyses, a mathematical method (the Fast Fourier Transform) is used to convert a block of EEG into a power spectrum so that concrete measures can be made. The power spectrum is split into standard frequency bands (delta, 1.5–3.5 Hz; theta, 3.5–7.5 Hz; alpha, 7.5–12.5 Hz; beta, 12.5–25 Hz) prior to measurement of absolute and relative power, mean frequency, and inter/ intrahemispheric coherence/symmetry. In a pilot study, baseline (pre-treatment) measures were compared for responders and nonresponders to medication (clomipramine or fluoxetine).¹¹ Responders showed increased power in the alpha and beta bands, particularly in frontal regions. Nonresponders showed decreased power in the delta band, particularly in the posterior regions. In a follow-up study, cluster analysis was used to identify subgroups.¹² Decreased power in the delta band was present in both subgroups of patients. Increased power in the theta band, particularly in anterior regions, defined patient group 1. Increased power in the alpha and beta bands, particularly in the anterior and central regions, defined patient group 2. Group 1 contained mostly nonresponders (71%), group 2 mostly responders (88%). These results were replicated in a larger study from the same group.¹³ The authors note that SSRIs have been shown to decrease alpha activity. Thus, SSRIs may help normalize brain activity in responsive patients. On the basis of these findings, they suggest that OCD may include pathophysiologically different subgroups with a similar clinical expression.

Event-related potentials (ERPs) are the summed electrical activity recorded from scalp electrodes following a stimulus presentation. A variety of situations or tasks are used in this type of study, and the ERP has a characteristic shape for each. A very simple task commonly used requires monitoring of a series of sounds (e.g., tones, words) and responding in some manner (e.g., pushing a button, keeping count) whenever a designated different sound is presented (“oddball” task). The different, rarely occurring, sound is the target. Early portions of the waveform are related to initial sensory reception and analysis of the stimulus. The subsequent N2 and P3 peaks relate to stimulus evaluation and perceptual synthesis.^14,15 In one set of studies, the latencies of the N2 and P3 peaks were shortened in patients who later responded well to SSRIs.^14,15 The amplitude of the N2 component may also be predictive of SSRI responsiveness, but in a complex manner. When simple sound stimuli (tones) were used, an increased N2 amplitude correlated with SSRI responsiveness.¹⁶ When complex sound stimuli (words) were used, decreased N2 amplitude correlated with SSRI responsiveness.^14,15 Thus, both accelerated processing and abnormal stimulus evaluation in this task may identify OCD patients likely to respond well to SSRIs. The authors note that these findings are consistent with the cortical hyperarousal model of OCD. These are promising results, but relatively few patients were studied, and the robustness and generalizability of the findings have not yet been demonstrated.

Two types of functional brain imaging have been used to study prediction of treatment response in OCD patients: positron emission tomography (PET) and single-photon emission computed tomography (SPECT). Functional imaging uses radioactive tracers followed by imaging to measure brain activity. All of the PET studies mentioned below used glucose utilization ([¹⁸F]fluorodeoxyglucose; FDG-PET) as their tool for examining brain function. The published SPECT study involved measurements of regional cerebral blood flow (rCBF) using [^99mTc]HMPAO. In comparison with SPECT, PET has better spatial resolution, but it is more expensive and not widely available. Although together these studies are very few in number, complex, and expensive to undertake, the concepts studied are exciting and the results are encouraging.

Unlike many other neuropsychiatric disorders, OCD has reasonably well established circuitry. However, a substantial subset of OCD patients remain treatment refractory. As with most psychiatric conditions, there are no robust predictive tests to guide sequencing of treatment trials. Although traditionally considered a single diagnostic entity, OCD is now thought to be an etiologically and phenotypically diverse condition.⁸ The heterogeneous nature of this severely disabling condition creates a substantial challenge. Pre-treatment indicators of likely treatment response would therefore be immensely valuable.

Previous studies of treatment prediction have not separated patients according to subtypes of OCD. Many have not included patients with comorbid disorders, although these are common. The meaning and interpretation of these studies might be different if more homogeneous subgroups of patients were compared. The great variety of approaches that has been used creates another challenge, making comparisons across studies difficult. Most studies have used relatively small groups and most have not been independently replicated. All studies to date were retrospective rather than prospective. Functional imaging studies have not all measured the same areas of brain, further complicating comparisons across studies.

Even with these problems, the reported results suggest that particular patterns of pre-treatment regional brain activity in OCD differentially predict treatment response (to behavioral therapy, SSRIs, adjunctive atypical antipsychotics, and neurosurgery). Prediction of treatment response based on pre-treatment brain activity could become increasingly important in the future for understanding the mechanisms of treatment response. This opens the door to an exciting new prospect of being able to use functional testing to help guide treatment.