First-in-human prediction of chronic pain state using intracranial neural biomarkers

Abstract

Chronic pain syndromes are often refractory to treatment and cause substantial suffering and disability. Pain severity is often measured through subjective report, while objective biomarkers that may guide diagnosis and treatment are lacking. Also, which brain activity underlies chronic pain on clinically relevant timescales, or how this relates to acute pain, remains unclear. Here four individuals with refractory neuropathic pain were implanted with chronic intracranial electrodes in the anterior cingulate cortex and orbitofrontal cortex (OFC). Participants reported pain metrics coincident with ambulatory, direct neural recordings obtained multiple times daily over months. We successfully predicted intraindividual chronic pain severity scores from neural activity with high sensitivity using machine learning methods. Chronic pain decoding relied on sustained power changes from the OFC, which tended to differ from transient patterns of activity associated with acute, evoked pain states during a task. Thus, intracranial OFC signals can be used to predict spontaneous, chronic pain state in patients.

Fig. 1 |. Long-term ambulatory tracking of chronic pain metrics.

a, Self-drawn body maps corresponding to the anatomical distribution of each of four participants’ spontaneous chronic pain location. Red and blue indicate areas of high and low pain, respectively. b, Scatterplot of an example participant’s report of overall pain intensity VASs over 105 d (mean 3.2 reports/d), with overlying moving average (red line; window = 3 samples), demonstrating a range larger than MCID. Each black point represents one pain report simultaneous with a neural recording. c, Histogram of each participant’s reported pain intensity NRS; most values were high (>6/10) but similar across participants. d, Group data demonstrating high correlation between VAS and NRS for pain intensity and unpleasantness across participants who reported them (CP3–4) with associated Pearson’s correlation R. e, Partial autocorrelation stem plots for each participant’s pain NRSs. Different pain score reporting frequencies for each participant resulted in different autocorrelation resolution. Bold stems indicate time lags achieving statistical significance (P < 0.05) based on two-sided 95% confidence intervals (for CP1–4, respectively: ±0.12, 0.14, 0.13 and 0.09) not corrected for multiple comparisons.

Fig. 2 |. Ambulatory neural recordings from ACC and OFC predict chronic pain state.

a, Example X-ray of a participant with bilateral implant of Activa PC + S DBS generators attached to depth leads in the ACC and paddle leads in the OFC (red highlights). b, Group localization of all electrode contacts in coronal (top) and sagittal (bottom) view. Blue shaded area is the ACC; yellow shaded area is OFC. Below are example raw LFP recordings from ACC (top) and OFC (bottom). c, Summary of the chronic pain-state decoding scheme for pain VAS using example data from one participant. Normalized power spectra are computed for each recording (ordered by increasing pain VAS (overlaid white circles) for display) and power values at each frequency are displayed across all recordings for an example participant. Above the color bar scale, a horizontal histogram of pain VAS shows the distribution of pain scores, which are split by the median value to define a dichotomous response variable (high (1) versus low (0) pain states). Average power values in frequency bands of interest serve as predictive features in two complementary decoding schemes. Power features sub-selected from either brain region or hemisphere are used to train models that decode high versus low pain states using LDA. d, Bar plots of decoding performance with successful pain-state prediction based on NRS for all participants using LDA. CP1–CP4 had n = 89, 137, 234 and 452 independent simultaneous recordings and pain score reports, respectively. One-sided empirical P values were calculated using permutation tests (n = 1,000; black dots), without correction for multiple comparisons (Methods) as reported in Supplementary Fig. 4 for all metrics and models. P values for NRS models in d from left to right are: CP1: 0.092, 0.801, 0.016; CP2: 0.001, 0.001, 0.001, 0.001, 0.001, 0.002, 0.001, 0.003, 0.777; CP3: 0.001, 0.005, 0.041, 0.001, 0.002, 0.027, 0.001, 0.001, 0.143; CP4: 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.612, 0.001. e, Normalized mean feature weights (importance) for each participant from full models (bilateral OFC/ACC) in d. (Note CP1 only has a unilateral implant. Contra, brain hemisphere contralateral to participants’ body side with chronic pain; ipsi, ipsilateral. ‡P < 0.05, *P < 0.01, †P < 0.001).

Fig. 3 |. Acute pain-state prediction with ACC and OFC neural recordings is unreliable.

Experimental scheme and decoding performance for acute, evoked pain state. a, Quantitative sensory testing thermode placement at most painful region on the side affected (aff-ACUTE) or unaffected (unaff-ACUTE) with chronic pain. b, Acute, thermal pain protocol (see text). c, Distribution of pain NRS during the task for all participants. d, Example temperature (blue) and NRS (red dot) data for one testing session. e,f, LDA decoder performance for high/low acute pain NRS is shown when thermal pain was applied on the side of the body either affected (e; for CP1–4; n = 13, 20, 25 and 25 independent trials, respectively) or unaffected (f, for CP1–4; n = 16, 16, 25 and 25 independent trials, respectively) by usual chronic pain. Gray points show chance level performance based on permutation tests (n = 1,000) used to calculate one-sided empirical P values without multiple-comparisons correction. Significant P values from left to right in e: CP1: 0.045; CP2: 0.012, 0.008, 0.037 and 0.013. See Supplementary Fig. 10 for all P values for e and f. g, Normalized mean feature weights (importance) from full models for the two participants that showed significant acute-affected pain decoding according to the color scale. *P < 0.05 (Supplementary Fig. 10).

Fig. 4 |. Decoding of chronic and acute pain states are differentially supported by ACC and OFC features across participants.

a, Relative importance of OFC versus ACC normalized power features to decoding of chronic pain NRS full models in each participant. Individual histograms show distributions of the magnitudes of OFC feature weights minus ACC feature weights across all recording sessions for real data (black, left facing) and shuffled surrogate data (gray, right facing; Methods). Values above 0 indicate greater OFC weights, and values below 0 indicate greater ACC weights. Contralateral OFC delta power was more important than ACC for discriminating high versus low pain across all participants (red highlight). b, Relative feature importance for acute-affected pain for the two participants that had significant decoding. For acute pain, there was a shift to greater ACC importance across frequencies compared with chronic pain. Two-sided Wilcoxon rank-sum test. P values corrected for multiple comparisons with the Benjamini–Hochberg method. In a, *P < 10⁻⁴; **P = 0.002 (CP1), **P = 0.043 (CP2), **P = 0.001 (CP3). In b, *P < 10⁻³; **P = 0.046 (CP1), **P = 0.042 (CP2).

Fig. 5 |. Temporal dynamics of power features distinguish chronic from acute pain states.

a, Example power time-series plot of a single feature (contralateral OFC delta) averaged across all recordings from low (blue line) versus high (red line) pain states, for one participant. Colored and shaded error bars show the s.e.m. The blue square in the upper-left corner represents that feature’s average weight as plotted in Fig. 2e (blue indicates negative weight ~ −1.0). This negative feature weight corresponds to decreased mean power during high pain states; periods of decreased power are highlighted with background gray shading. b, Box plots with overlying raw data of the percentage of total recording clip time on which increases or decreases occurred for chronic pain features (black dots) and acute pain features (red dots). c, Mean duration of bouts of increases and decreases similarly. d, Number of increases or decreases per second for chronic and acute pain features; the symbol legend in d applies to b–d. In b–d for CP1–4 chronic pain features, respectively, n = 215, 455, 880 and 1,765 independent power time series. For CP1 and CP2 acute pain features, respectively, n = 35 and 60 independent power time series. Box plot bounds indicate the 25th and 75th percentiles, the pink line shows median, and whiskers show the full extent of data from minima to maxima with points outside the whiskers considered outliers. Two-sided Wilcoxon rank-sum tests with correction for multiple comparisons. In c, Z_CP1 = 7.7, *P = 10⁻¹⁴; Z_CP2 = 11.6, *P = 10⁻³⁰. In d, Z_CP1 = −8.9, *P = 10⁻¹⁹; Z_CP2 = −12.5, *P = 10⁻³⁵. See Supplementary Fig. 13 for additional details and the top five features per participant.