The cortical rhythms of chronic back pain

Abstract

Chronic pain is maladaptive and influences brain function and behavior by altering the flow and integration of information across brain regions. Here we use a power spectral analysis to investigate impact of presence of chronic pain on brain oscillatory activity in humans. We examine changes in BOLD fluctuations, across different frequencies, in chronic back pain (CBP) patients (n = 15) as compared to healthy controls (n = 15) during resting-state fMRI. While healthy subjects exhibited a specific, frequency band-dependent, large-scale neural organization, patients showed increased high-frequency BOLD oscillations (0.12-0.20 Hz) circumscribed mainly to medial prefrontal cortex (mPFC) and parts of the default mode network. In the patients a correlation analysis related the mPFC aberrant BOLD high-frequency dynamics to altered functional connectivity to pain signaling/modulating brain regions, thus linking BOLD frequency changes to function. We also found that increased frequency fluctuations within the mPFC were temporally synchronous with spontaneous pain changes in patients during a pain-rating task. These observations provide novel insights about the nature of CBP, identifying how it disturbs the resting brain, and link high-frequency BOLD oscillations to perception.

Figure 1.

Spatial distribution of spectral power for BOLD oscillations in healthy subjects and CBP during resting-state fMRI. A, Brain maps show spatial distribution of the group average spectral power for the three frequency bands (low: 0.01–0.05 Hz, middle: 0.05–0.12 Hz, and high: 0.12–0.20 Hz) for CBP patients (n = 15) and healthy controls (n = 15). In the control group, the lowest frequency exhibited highest power, and was localized mainly to prefrontal, parietal, and occipital cortices. The mid- and high-frequency bands consecutively exhibited less power, and localized more within cingulate, insular, and temporal cortices, in addition to subcortical structures. The CBP group generally shows a similar pattern of spatial distribution of spectral power to the healthy group, with increases in the high-frequency power in parts of the medial frontal and insular regions and mid-frequency power in the cingulate cortex. B, Group average histograms of power spectral density (PSD) for BOLD oscillations for the three frequency bands in healthy subjects (black trace) and CBP (gray trace).

Figure 2.

Regional differences in power spectral density (PSD) between healthy subjects and CBP during resting-state fMRI. A, Whole-brain voxelwise differences in power for the high-frequency band between CBP and healthy subjects. Brain areas in red-yellow depict statistically significantly higher power in CBP compared to controls (unpaired t test, random-effects model, z-score>3.0, cluster p < 0.01, corrected for multiple comparisons), localized mainly to the mPFC, PCC, and bilateral LP. No significant differences were detected for the mid- and low-frequency bands. Brain images are in standard MNI space, and coordinates are in millimeters. B, Individual BOLD time courses from the mPFC for healthy subjects (left) and CBP (right). C, Individual power spectra for the mPFC BOLD time courses superimposed separately for healthy subjects (black traces) and CBP (gray traces). Red traces represent group averages. D, Bar graphs show the mean ± SD spectral power from the mPFC time courses for the three frequency bands in healthy subjects (black bars) and CBP (gray bars). CBP exhibited significant decrease in the PSD for the low-frequency (LF) band, no change for the mid-frequency (MF) band, and an increase in the high-frequency (HF) band. [Compare to Malinen et al. (2010), their Figs. 2–4.]

Figure 3.

Functional connectivity differences for the mPFC between healthy subjects and CBP during resting-state fMRI. A, Whole-brain voxelwise contrast in mPFC connectivity between CBP and healthy subjects. Brain regions in red–yellow depict statistically significant increased mPFC connectivity in CBP compared to controls (unpaired t test, random-effects model, z-score > 3.0, cluster p < 0.01, corrected for multiple comparisons). CBP showed increased correlation between the mPFC and ACC, INS, and S2, regions known to be involved in pain perception and modulation. Brain images are in standard MNI space, and coordinates are in millimeters. B, Bar graphs show the mean ± SD of normalized correlation values (z-scores) between mPFC and ACC BOLD time courses (6 mm ROI) for healthy subjects (black bars) and CBP (gray bars). The mPFC showed a negative correlation with the ACC in healthy subjects. This relationship was significantly reversed in CBP. Scatter plot depicts the relationship between the high-frequency spectral power (HF-PSD) for the mPFC BOLD time courses and the strength of mPFC–ACC correlations in healthy subjects (black triangles) and CBP (gray circles). The strength of the mPFC–ACC correlation showed a significant positive relationship with mPFC BOLD HF-PSD in CBP (r = 0.83, p < 0.01) but not in healthy subjects (r = −0.05, p = 0.85). C, D, Same as B for mPFC-INS and mPFC-S2 correlations. **p < 0.01.

Figure 4.

Relationship between mPFC BOLD spectral power and spontaneous pain ratings in CBP. A, Spontaneous pain ratings for 13 CBP patients. Patients used a finger-span device to continuously rate spontaneous fluctuations of their back pain (pain rating task) on a scale of 0–100 in the absence of external stimulation during fMRI scanning. In a separate scan, patients also performed a visual control task, in which they rated the size of a bar projected on a screen. B, An example of the mPFC BOLD time course from CBP patient 07 (pat 07) during the pain-rating task. C, Top color panel shows the spectrogram of the mPFC BOLD signal. Lower line plots show the averaged normalized spectral power for the low- (LF, 0.01–0.05 Hz, blue trace), mid- (MF, 0.05–0.12 Hz, green trace), and high- (HF, 0.12–0.25, Hz, red trace) frequency bands derived from the spectrogram. Power was averaged across all frequencies for a given band and normalized with respect to the mean. D, Spontaneous pain rating for patient 7, after convolving with homodynamic response function and temporal resampling to match the time course of the spectrogram. The pain rating exhibits similar temporal fluctuations only to the high-frequency band spectrum. E, Bar graphs show the mean ± SD Pearson correlation coefficients of the ratings and power spectra for the three frequency bands during pain rating (black bars) and visual rating (white bars) tasks. Individual patients' ratings showed a significant relationship only to the mPFC power for the high-frequency band and only for pain rating task. **p < 0.01.