Control over brain activation and pain learned by using real-time functional MRI

Abstract

If an individual can learn to directly control activation of localized regions within the brain, this approach might provide control over the neurophysiological mechanisms that mediate behavior and cognition and could potentially provide a different route for treating disease. Control over the endogenous pain modulatory system is a particularly important target because it could enable a unique mechanism for clinical control over pain. Here, we found that by using real-time functional MRI (rtfMRI) to guide training, subjects were able to learn to control activation in the rostral anterior cingulate cortex (rACC), a region putatively involved in pain perception and regulation. When subjects deliberately induced increases or decreases in rACC fMRI activation, there was a corresponding change in the perception of pain caused by an applied noxious thermal stimulus. Control experiments demonstrated that this effect was not observed after similar training conducted without rtfMRI information, or using rtfMRI information derived from a different brain region, or sham rtfMRI information derived previously from a different subject. Chronic pain patients were also trained to control activation in rACC and reported decreases in the ongoing level of chronic pain after training. These findings show that individuals can gain voluntary control over activation in a specific brain region given appropriate training, that voluntary control over activation in rACC leads to control over pain perception, and that these effects were powerful enough to impact severe, chronic clinical pain.

Figures

Fig. 1.

Pain control task. (A) Task diagram (STIM not present for pain patients). (B) Scrolling line chart of rtfMRI activation viewed by subjects during training. Chart units are percent signal change for BOLD signal (fMRI BOLD) vs. time in seconds. (C) Two sample images taken from a continuum of video images presented to subjects depicting low (Left) to high (Right) levels of activation in the target ROI, corresponding to the arrows in B.

Fig. 2.

Volumetric analysis of the spatial pattern of learned control over activation. (A) Change in activation comparing the last training session to the first training session showing activation in rACC, the targeted brain region. Seven total clusters were observed at this threshold level (t > 12.80, top of scale t = 18.00; for coordinates, see Table 1, which is published as supporting information on the PNAS web site). (B) Repeat of the same analysis comparing the posttest session (performed after the last training session) to the initial training session, showing similar results. Data are presented as thresholded, Bonferroni-corrected t-maps superimposed on high-resolution T1 data. The crosshairs indicate the three planes of section displayed and the group mean of the target ROI y and z coordinates used for rACC rtfMRI-based training (x coordinate for training ROI was midline). Color designates the t value, using a general linear model comparing different time periods convolved with a canonical hemodynamic response function. All data are experimental group averages after normalization to Talairach-Tournoux coordinates.

Fig. 3.

Learned enhancement of control over fMRI BOLD activation and pain. (A) Control over fMRI BOLD activation in rACC ROI activation increased significantly through training (*, P < 0.05, linear regression; †, P < 0.05, t test run 3/4 vs. run 1). (B) In parallel, control over pain increased significantly through training (*, P < 0.05, linear regression; †, P < 0.05, t test run 3/4 vs. run 1). (C) The difference in BOLD activation induced by the subject correlated with the difference in reported pain intensity (P < 0.00076, linear regression) for each individual cycle during which subjects increased and then decreased brain activation and rated the intensity of individual stimuli (all experimental subjects). fMRI BOLD plotted in A is percent signal change, measured as the group mean and standard errors of the difference in T2*-weighted MRI intensity during stimuli presented during increase periods vs. during decrease periods, shifted by5sto allow for hemodynamic delay and averaged over all voxels within the ROI and averaged over five repeated blocks per training run. Bars in B represent the group mean and standard errors of a pain intensity percentage difference index, defined as 100% × (Rinc - Rdec)/((Rinc + Rdec)/2), where Rinc and Rdec correspond to the pain rating for increase and decrease periods, respectively.

Fig. 4.

Percentage change in control over perceived pain intensity and unpleasantness for experimental group and four comparison control groups. Training included 36 total subjects among all groups and 140 total pain training, posttesting, and scanning runs. Each bar plots the group mean and standard errors of percentage change in pain intensity difference ratings (open bars) or pain unpleasantness difference ratings (filled bars). These values correspond to the change in the pain intensity percentage difference index, as defined in Fig. 2, between run 1 and the average of runs 3/4. Results were similar when runs 3 and 4 were analyzed individually. †, t test for experimental group; *, paired t test compared with experimental group for control groups.

Fig. 5.

Changes in pain ratings and rACC activation in chronic pain patients after rtfMRI-based training. (A) Change in experimental and control subject pain ratings after vs. before training. Error bars correspond to standard errors of group means. (B and C) Significant correlation (P < 0.01, linear regression) between individual subject percentage change in MPQ pain rating and VAS pain ratings, respectively, and changes in rACC ROI fMRI BOLD activation (change in signal intensity from increase vs. decrease periods taken from the last vs. the first training run).