We have described a novel technique (STAR) for providing robust regional real-time fMRI feedback, and have evaluated the method retrospectively in 19 subjects using a paradigm based on alternating sets of thoughts (motor repetition vs. spatial navigation). All subjects were able to achieve rapid and accurate cursor control with the (PLS-based) whole-brain feedback, with an average (per frame) classification accuracy of 83%. Retrospectively, the regional STAR feedback within five a priori regions of interest (SMA, PPA-R, PPA-L, RC-R, RC-L) was also determined to be relatively robust. With average classification accuracies above 70%, the STAR technique performed significantly better than a regional BOLD feedback approach for all five regions, while maintaining spatially localized information.
Our approach addresses the need for noise suppression in real-time feedback applications, particularly when the feedback is localized to a small sub-region of the brain. Conventional fMRI involves averaging of data collected over an entire scan in order to generate a parametric map showing locations of task-related activity. In contrast, real-time feedback applications require measurement of brain activity at every data frame (TR on the order of 2 seconds), and are thus more susceptible to noise. By combining information from pixels throughout the brain, the STAR method is able to achieve the desired noise reduction without significantly impacting regional specificity.
The STAR approach can be incorporated into a localized self-regulation training protocol (in which participants are instructed to alternate between two cognitive tasks) in the following manner. First, regions of interest are selected from the training STAR map (functional localizer), which is developed during the initial (3–6 minute) portion of the scan. The training STAR map displays (cross-validated) logits, or predicted probabilities, with bright pixels corresponding to regions where real-time feedback is expected to be robust (i.e. discriminating well between the two states). If a suprathreshold activation (bright spot) appears within a target region of interest (e.g., the insula for learning control of an emotional arousal response), then the operator would manually select that region. Next, the subject is shown STAR feedback from the selected region(s), and attempts to increase control of the feedback cursor over time. The progress can be seen (in real time during the scan) by the operator as well as by the participant using either the feedback time series or the dynamic feedback STAR map. Alternatively, if no suprathreshold pixels (bright spots) appear within the targeted anatomic region, the operator may elect to extend the classifier-training period or restart the scan (perhaps with updated instructions or with modified experimental parameters).
As mentioned in section 2.3, this study did not focus on evaluating feedback efficacy. Therefore, although the subjects could maintain good control of the feedback marker presented during the scan, it remains unclear whether they could have performed equally well without seeing the feedback marker at all. This will be the topic of future studies, where we expect various factors, including task difficulty, nature of the feedback, and fatigue/boredom will play a role in predicting efficacy.
A limitation of the technique is that it depends on a training pre-scan (or classifier-training portion of the scan) to develop the model. Fortunately, at least in the present application, this seems to only require 3–6 minutes of scan time. The large benefit of this small time investment is that the investigator can immediately determine which brain regions are (indeed) participating in the task, and can then choose the exact anatomical regions to be used for the feedback run, tailored to the individual. This empirical approach enables precise selection of regions for feedback, without requiring a priori omniscience regarding ROIs. This empirical determination of ROIs can facilitate the training process for complex tasks in which the relative participation of hypothesized brain regions, for a given individual, is uncertain -- and may actually be the target of the investigation. However, we note that the technique cannot track any activity during feedback which is not (at least to some extent) exhibited during the initial classifier-training period, possibly limiting the application of the methodology. This is in contrast to the BOLD method which does not require acquisition of training data. Nevertheless, in cases where there does not appear to be sufficient activity during training, the classifier-training session could either be restarted or lengthened in order to obtain a sufficiently robust classification model for feedback.
The current approach is best suited for localized brain regions that are geographically distinct (rather than overlapping, or anatomically interconnected), for which the subject has the potential ability to modulate activity (with respect to 30 second task periods), and whose activity does not “carry over” beyond the instructed task period. Brain regions whose activity, once triggered, is persistent or even recruiting (e.g., limbic regions triggered by exposure to drug or sexual cues [30
]) pose a challenge for all “comparison-based” real-time approaches, including (the simple subtraction between alternating states in) regional BOLD, classifier-based approaches, and STAR. For brain regions that “stay on” past the task period, other kinds of feedback strategies that do not depend upon baseline or comparator states (e.g., connectivity) may offer future utility as a feedback signal. Furthermore, the present technique cannot, in its current form, be applied to event-related task designs, or other paradigms where the shape of the hemodynamic response function must be considered.
Although the advantage over regional BOLD was found to be statistically significant in all regions, we note that we have only explored a few of the most common pre-processing options available for the BOLD signal time series. Other techniques such as physiologic noise reduction and advanced motion correction may further improve the performance of the BOLD method.
As a final note, for the majority of subjects, the visual feedback was perceived as helpful for maintaining cognitive control in the “tennis” vs. “spatial navigation” tasks. However, some individuals found it relatively easy to alternate brain states in this task, without feedback. For these subjects, the addition of feedback was initially perceived as unnecessary, and in some cases, distracting. This suggests that task difficulty and subject ability may determine whether real-time fMRI feedback is perceived as beneficial. Future real-time studies should take this into account, matching (when possible) task difficulty and feedback format to the target population.