Here we present a method for training people to control a brain area involved in contamination anxiety and for probing the relationship between contamination anxiety and brain connectivity patterns.
We present a method for training subjects to control activity in a region of their orbitofrontal cortex associated with contamination anxiety using biofeedback of real-time functional magnetic resonance imaging (rt-fMRI) data. Increased activity of this region is seen in relationship with contamination anxiety both in control subjects1 and in individuals with obsessive-compulsive disorder (OCD),2 a relatively common and often debilitating psychiatric disorder involving contamination anxiety. Although many brain regions have been implicated in OCD, abnormality in the orbitofrontal cortex (OFC) is one of the most consistent findings.3, 4 Furthermore, hyperactivity in the OFC has been found to correlate with OCD symptom severity5 and decreases in hyperactivity in this region have been reported to correlate with decreased symptom severity.6 Therefore, the ability to control this brain area may translate into clinical improvements in obsessive-compulsive symptoms including contamination anxiety. Biofeedback of rt-fMRI data is a new technique in which the temporal pattern of activity in a specific region (or associated with a specific distributed pattern of brain activity) in a subject’s brain is provided as a feedback signal to the subject. Recent reports indicate that people are able to develop control over the activity of specific brain areas when provided with rt-fMRI biofeedback.7-12 In particular, several studies using this technique to target brain areas involved in emotion processing have reported success in training subjects to control these regions.13-18 In several cases, rt-fMRI biofeedback training has been reported to induce cognitive, emotional, or clinical changes in subjects.8, 9, 13, 19 Here we illustrate this technique as applied to the treatment of contamination anxiety in healthy subjects. This biofeedback intervention will be a valuable basic research tool: it allows researchers to perturb brain function, measure the resulting changes in brain dynamics and relate those to changes in contamination anxiety or other behavioral measures. In addition, the establishment of this method serves as a first step towards the investigation of fMRI-based biofeedback as a therapeutic intervention for OCD. Given that approximately a quarter of patients with OCD receive little benefit from the currently available forms of treatment,20-22 and that those who do benefit rarely recover completely, new approaches for treating this population are urgently needed.
1. Stimulus Development
Extensive stimulus development is needed. Contamination-related and neutral images must be collected and piloted to ensure the anxiety induced by these stimuli is balanced across provocation conditions and significantly greater in the provocation conditions than in the neutral conditions. More specifically, the following four stimulus sets are needed:
The stimuli used by our group include images from the Maudsley Obsessive Compulsive Symptom Set 23 and the International Affective Picture System 24, as well as photographs we took ourselves, acquired from Google images, and purchased from Bigstockphoto.com, gettyimages.com, flickr.com, and iStockphoto.com.
2. Recruitment
Subjects are screened to identify healthy individuals who can participate in magnetic resonance imaging and who report high levels of contamination anxiety and a desire to learn to control that anxiety. In particular, as part of the screening process, subjects complete the Padua Inventory-Washington Slate University Revision (PI-WSUR)25 and only those with a score of 8 or greater on the Obsessions and Washing Compulsions Subscale are included in the study. For each subject that receives true biofeedback, another subject matched in age and gender is recruited to receive sham biofeedback. Prior to participation, all subjects must give informed consent in accordance with a protocol approved by the institutional human protection program (at Yale University, this is the Human Research Protection Program).
3. Protocol
The aim of the biofeedback protocol is to train subjects to develop greater control over the neural activity level in a region of their orbitofrontal cortex (OFC) related to contamination anxiety, so that, when they are exposed to contamination-related stimuli, they can increase or decrease neural activity in this region as they wish. We hypothesize that greater control over this brain area will give subjects greater control over their contamination-related anxiety. This ability, to consciously control the neural activity level in the OFC, is assessed based on whether subjects are able to increase and decrease the signal measured from this brain area when they are cued to increase and decrease that activity during a functional imaging session.
Subjects come in on four separate days, scheduled at approximately half-week intervals, so that the entire study takes two weeks to complete. The flowchart for the protocol is shown in Figure 1.
Figure 1. Flowchart of protocol. Day 1 is shown blue, Day 2 in red, Day 3 in green and Day 4 in orange. Although not explicitly listed, each MR session also includes the collection of anatomical data in the same slice locations as the functional data and the biofeedback MR sessions include the collection of a “functional reference scan” used to register the target region to the functional space of that session.
3.1 Day 1
3.2 Day 2
3.3 Day 3: Identical to Day 2 but using separate (matched) sets of stimuli.
3.4 Day 4
4. Debriefing of Sham Subjects
Upon completion of the study, all sham participants are informed that they received sham feedback and debriefed to ensure they are not upset about the deception, and to check if they suspected that the feedback they were receiving was not veridical.
5. Off-line Data Analyses
5.1 Three primary outcome measures are computed for each subject:
5.2 Group level analyses
At a minimum, the following are examined at the group level:
Both the offline analyses and the real-time analyses described in this manuscript are conducted using BioImage Suite (www.bioimagesuite.org). This software package is freely available and open source. The real-time analysis component, although not available on-line, is available upon request. It is designed to decouple the real-time data analysis from the display program, so the latter can be altered without requiring modification of the former. This allows for flexibility in experimental design, for example, the display program can be written using any of the standard software (e.g., E-prime, Matlab, Presentation). In addition, the real-time analysis employs graphics processing unit accelerated motion correction, which enables high quality motion correction with almost no processing delays. This system is described in more detail in Scheinost et al., 2011.26
6. Representative Results
A subject who gains control over their target brain area during the biofeedback should have an increase in control over the target brain area, as assessed during the control task runs, and this should translate into a reduction in contamination anxiety during the Assessment Sessions. Figure 3 shows a screen shot of the visual display from one of the final biofeedback runs of a subject who successfully gained control over their OFC. The success of this subject in controlling the region during this run is reflected by the fact that the line graph is higher during the red periods than the blue periods, particularly after adjusting for the expected six to eight second delay. This same subject showed increased control as assessed during his control task runs (from an average beta value of 0.003 to an average beta value of 0.23) as well as a significant decrease in anxiety in response to contamination images presented in the Assessment Sessions (p<0.005) as shown in Figure 4. This was a successful subject. In contrast, other subjects did not learn to control the target region, and did not show any decreases in contamination anxiety as assessed in the Assessment Sessions. In general, we find large variability across subjects in their ability to learn to control this region.
Figure 3. Screen shot of the visual display viewed during a biofeedback run, taken at the end of the run. Because the run ends with the neutral condition, the image viewed at the time of the screen dump (in this case, the picture of the books) is neutral, and the arrow is white and pointing forward. During increase and decrease blocks, contamination related images were shown. The arrow on the left was a red up arrow during the increase blocks and a blue down arrow during the decrease blocks. The line graph at the bottom of the display represents OFC activity during the run. The color of the line indicates which kind of block was occurring during that time period of the scan (red for increase, blue for decrease, and white for neutral). The graph covers the time frame from the moment the first volume is processed (approximately 3s after the start of the run) until the time the 128th volume is processed (approximately 257s after the start of the run). The y-axis indicates percent signal change from the running mean in the OFC minus the percent signal change from the running mean in the white matter control ROI (in this run, amplitudes ranged between 2.1 and -3.7). Note that after accounting for a 6-8s delay (corresponding to 3-4 time points), activity in this region was greater during red than blue periods, reflecting the success of this subject in controlling the region. The sham subject matched to this subject would see identical stimuli, however, in the case of the sham subject, the line graph would not be related to their true pattern of brain activity.
Figure 4. Bar graph summarizing self-reported anxiety ratings in response to contamination images in (a) the first Assessment Session (before the biofeedback) and (b) the final Assessment Session (after the biofeedback) from the subject whose biofeedback time-course is shown in Figure 3. This subject reported significantly lower anxiety after the biofeedback as indicated by the asterisk.
Biofeedback of real-time fMRI data is a new technique and more work is needed to optimize this method so as to maximize learning in subjects. Recent studies have explored how learning changes with different numbers of runs or scanning sessions,14, 18, 27 how the feedback paradigm affects learning28, and whether the learning induced by a given biofeedback protocol results in changes in brain function that persist beyond the end of the biofeedback training period.15, 18, 27, 29 However, a great deal more work along these lines is needed, bearing in mind that the optimal protocol may vary depending on the brain area targeted, the population studied, and other variables.
One challenge faced in neurofeedback studies is the optimal way to control for practice, exposure, motivation and placebo effects. There are a variety of approaches that have been described in the literature, each of which has its advantages and disadvantages. In this protocol, a sham biofeedback paradigm is employed in which control subjects receive identical stimuli as their matched biofeedback subjects and are led to believe that they are receiving true biofeedback based on their own brain activity patterns. This approach has the advantage that the instructions and stimuli are controlled for. It also helps to control for motivation and placebo effects. That is, neurofeedback induces in many subjects a game-like mentality in which they become personally invested in their performance. The sham control condition duplicates that experience as closely as possible, thus controlling for the high level of motivation of the neurofeedback subjects. Furthermore, if a subject receives feedback indicating increasing success, the resulting sense of achievement and perception of self-control can translate into placebo effects on behavioral measures. Once again, the sham paradigm we used controls for this possibility as effectively as possible. However, a drawback to this sham biofeedback approach is that it actively misleads subjects and could thus interfere with the learning that would normally occur during periods of practice without feedback. Another kind of control condition used for neurofeedback studies is to have subjects perform the same task without neurofeedback. This controls for practice and exposure effects, and does not have the drawback of misleading and possibly confusing self-reflection based learning processes. However, it may not control as well for motivation and placebo effects. Another form of sham biofeedback has also been used in which subjects receive information regarding another brain area that is not thought to be involved in the task, although subjects are misled to believe their target area is relevant to the task. This approach involves an assumption on the part of the researchers regarding a region of the brain unrelated to the task, and this can be problematic if the region turns out to be involved in the task. Furthermore, if the region really is irrelevant to the task, the sham subjects are unlikely to have success controlling it, and are thus likely to feel disappointed and frustrated in contrast to the true feedback subjects who are more likely to experience success and feel satisfied and in control. Thus, this form of sham biofeedback does not control as well for the emotional state of the subject (and thus the motivation and placebo effects) as the type of sham described in this manuscript, and has the same drawback of possibly interfering with the learning process by providing misinformation. Finally, control conditions in which the control subjects receive an alternative form of treatment outside of the magnet (such as cognitive-behavioural therapy) can be used to contrast the effectiveness of rt-fMRI biofeedback with whatever is the gold standard in terms of treatments at present. This last approach does not attempt to control precisely for all the effects occurring during neurofeedback, and thus does not address whether it is the feedback per se that induces behavioural improvements, but rather asks the important question: taken all together, can real-time fMRI biofeedback as an intervention produce better clinical or behavioural results than the current alternatives? In summary, the choice of which type of control to use in a biofeedback study is an important and challenging aspect of the study design, and the limitations of the control condition used need to be taken into account when interpreting the results.
Although still in a developmental stage, the therapeutic use of biofeedback of real-time fMRI has potential utility for a range of neuropsychiatric conditions. Furthermore, when used in conjunction with assessments of functional brain organization and cognitive/clinical variables (collected before and after the biofeedback), it can be a powerful research tool. Essentially, biofeedback provides a low-risk “perturb and measure” approach to studying the neural basis of human mental function: the biofeedback is used to perturb the functional organization of the brain and the resulting changes in mental function are measured. Given both its research and clinical potential, this is a promising new technology for the fields of psychiatry and cognitive neuroscience.
The protocol described here investigates whether biofeedback of real-time fMRI can help healthy subjects gain control over their contamination anxiety. Although it is possible that the neural substrate of contamination anxiety in healthy controls is different from that in OCD patients, it is also possible that obsessive-compulsive symptom dimensions run through both the healthy and patient populations, and that similar mechanisms underlie contamination anxiety in both groups. If so, and if biofeedback of real-time fMRI is effective in helping healthy subjects control their anxiety, a similar paradigm may have clinical utility for obsessive-compulsive disorder.
The authors have nothing to disclose.
This study is funded by NIH (R21 MH090384, R01 EB006494, RO1 EB009666, R01 NS051622). We thank H. Sarofin and C. Lacadie for their technical assistance.