Beyond feeling: chronic pain hurts the brain, disrupting the default-mode network dynamics

Abstract

Chronic pain patients suffer from more than just pain; depression and anxiety, sleep disturbances, and decision-making abnormalities (Apkarian et al., 2004a) also significantly diminish their quality of life. Recent studies have demonstrated that chronic pain harms cortical areas unrelated to pain (Apkarian et al., 2004b; Acerra and Moseley, 2005), but whether these structural impairments and behavioral deficits are connected by a single mechanism is as of yet unknown. Here we propose that long-term pain alters the functional connectivity of cortical regions known to be active at rest, i.e., the components of the "default mode network" (DMN). This DMN (Raichle et al., 2001; Greicius et al., 2003; Vincent et al., 2007) is marked by balanced positive and negative correlations between activity in component brain regions. In several disorders, however this balance is disrupted (Fox and Raichle, 2007). Using well validated functional magnetic resonance imaging (fMRI) paradigms to study the DMN (Fox et al., 2005), we investigated whether the impairments of chronic pain patients could be rooted in disturbed DMN dynamics. Studying with fMRI a group of chronic back pain (CBP) patients and healthy controls while executing a simple visual attention task, we discovered that CBP patients, despite performing the task equally well as controls, displayed reduced deactivation in several key DMN regions. These findings demonstrate that chronic pain has a widespread impact on overall brain function, and suggest that disruptions of the DMN may underlie the cognitive and behavioral impairments accompanying chronic pain.

Figures

Figure 1.

Task design and brain activity differences between CBP patients and normal controls. a, Illustration of the changing vertical height of the target (gray trace) and an example of its height tracking with the modified joystick (black trace; shifted 10 units for ease of visualization) in a subject performing the visual attention tracking task. The active/rest time plot shown below represents the vector (after convolving with the hemodynamic response function) used to identify brain regions where the BOLD signal was activated or deactivated during the task. Periods of active tracking were modeled either as +1 or −1 to identify task-activated or task-deactivated regions respectively, relative to periods of rest, which were modeled as 0. Right inset, Group-averaged correlation coefficients ± SD between the target course and its tracking demonstrate that the two groups performed the task similarly. b, Group-averaged activations (red–yellow) and deactivations (blue–green) in CBP and healthy controls (random effects z > 2.3, cluster p < 0.01; overlaid on standard space). Activations were comparable between the two groups, whereas deactivations were more extensive in the controls. The last column shows the contrast (t test; p < 0.01) between controls and CBP patients. CBP patients exhibit less deactivation than normal subjects mainly in mPFC, amygdala, and PCC, all of which are considered part of the DMN.

Figure 2.

Differences in time course of BOLD signal between CBP patients and healthy controls. a, Group-averaged BOLD signals from mPFC (blue) and LIPS (red) for controls (top) and CBP patients (bottom) are shown superimposed on the respective group-averaged tracking time courses (gray). The main result illustrated here is that mPFC BOLD signal is more deactivated in normal subjects than in CBP patients each time the subject engages in tracking. The arrow indicates a time in which these differences can be appreciated even by simple inspection of the traces. b, Time course of average BOLD responses for mPFC (top) and LIPS (bottom) relative to transition from rest to tracking. Task-triggered BOLD signals averaged over 135 tracking events were significantly smaller in mPFC of CBP patients selectively in the deactivation phase (*p < 0.01), at times of peak tracking (10–20 s from start of tracking), whereas task performance (magnitude tracking) did not differ between the two groups (top right). The LIPS responses were similar between the groups. Middle, Cross-correlations between BOLD signal and tracking time course for each group for different time lags (−10 to 10 s) revealed that the mPFC BOLD signal was anticorrelated to the task time course in normal subjects (mean r = −0.35 ± 0.2, SEM, at lag = 2.5 s), and that this anticorrelation was significantly attenuated (mean r = −0.11 ± 0.20, SEM, at lag = −2.5 s; p < 0.001) in CBP patients. LIPS signal was positively correlated to task execution and did not differ between the groups.

Figure 3.

Disrupted correlation maps in CBP patients. Group-averaged z-score maps (n = 15 in each group) showing regions with significant correlations with the six seed regions (small circles) in normal controls (left) and in CBP patients (right). Results are shown for the three task-negative seed regions (mPFC, PCC, and LP) and three task-positive seed regions (IPS, FEF, and MT). Regions with positive correlations (red–yellow) have z scores >2.3 (p < 0.01), and those with negative correlations (blue–green) have z scores less than −2.3 (p < 0.01). Notice that in the CBP patients' map, the majority of regions identified in the control subjects as being negatively correlated (i.e., blue colored) are missing.

Figure 4.

The ratio between the number of voxels with positive versus negative correlations (means ± SEM) is close to unity in healthy subjects for all the seeds but significantly larger in CBP patients (*p < 0.01, two-sampled t test).