Deciphering neuronal population codes for acute thermal pain

Abstract

Objective: Pain is defined as an unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage. Current pain research mostly focuses on molecular and synaptic changes at the spinal and peripheral levels. However, a complete understanding of pain mechanisms requires the physiological study of the neocortex. Our goal is to apply a neural decoding approach to read out the onset of acute thermal pain signals, which can be used for brain-machine interface.

Approach: We used micro wire arrays to record ensemble neuronal activities from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) in freely behaving rats. We further investigated neural codes for acute thermal pain at both single-cell and population levels. To detect the onset of acute thermal pain signals, we developed a novel latent state-space framework to decipher the sorted or unsorted S1 and ACC ensemble spike activities, which reveal information about the onset of pain signals.

Main results: The state space analysis allows us to uncover a latent state process that drives the observed ensemble spike activity, and to further detect the 'neuronal threshold' for acute thermal pain on a single-trial basis. Our method achieved good detection performance in sensitivity and specificity. In addition, our results suggested that an optimal strategy for detecting the onset of acute thermal pain signals may be based on combined evidence from S1 and ACC population codes.

Significance: Our study is the first to detect the onset of acute pain signals based on neuronal ensemble spike activity. It is important from a mechanistic viewpoint as it relates to the significance of S1 and ACC activities in the regulation of the acute pain onset.

Figure 1

Histology and electrode placement. (A) The electrode tip (in circle) at the end of recording experiments was seen in the rat's ACC. (B) Trace mark (in circle) shows the electrode placed in the rat's S1 hindlimb. Scale bar 1 mm.

Figure 2

Raster plots and peri-event time histograms (PETHs) of pain-modulated S1 and ACC units (250 mW laser intensity). (A) S1 positive responder. (B) S1 negative responder. (C) ACC positive responder. (D) ACC negative responder. In all plots, spikes are aligned with paw withdrawal. Temporal bin size 50 ms. The waveforms from four channels and total spike counts are shown on the top of rasters. Arrow in the each row of rasters indicates the laser onset of each trial.

Figure 3

Single-trial decoding analysis in a S1 population (left, 12 units, Dataset rat1-S1-1) and an ACC population (right, 7 units, Dataset rat4-ACC-1). ((A), (D)) Sorted population spike counts. Bin size 50 ms. Color bar indicates spike count, with the dark color representing large spike count. ((B), (E)) Estimated mean Z-score (blue curve) from the univariate latent state. The vertical red lines indicate the paw withdrawal. Horizontal dashed lines mark the thresholds of the significant zone. The shaded area marks the confidence intervals, and the red curve marks the empirical Z-score computed directly from the multi-unit spike count. ((C), (F)) Equivalent p-value derived from the mean Z-score (blue curve) of panels (B) and (E), respectively.

Figure 4

Single-trial decoding examples in S1 (left: 12 units, Dataset rat3-S1-3) and ACC populations (right: 15 units, Dataset rat5-ACC-2). (A) Sorted population spike counts. Bin size 50 ms. The color bar indicates spike count, with dark color representing large spike count. (B) Estimated mean Z-score (blue curve) from the univariate latent state variable. The vertical red line indicates the paw withdrawal. Horizontal dashed lines mark the thresholds of significant zone. The shaded area marks the confidence intervals, and the red curve marks the empirical Z-score computed directly from the multi-unit spike count.

Figure 5

Alternative detection strategy for detecting acute pain signals in an ACC population (7 units, Dataset rat4-ACC-1). (A) Sorted population spike counts (same as figure 3(A)). Bin size 50 ms. The color bar indicates spike count, with dark color representing large spike count. (B) Arithmetic mean of absolute Z-scores from all 7 units. (C) Geometric mean of absolute Z-scores from all 7 units. (D) Maximum Z-score derived from all 7 units.

Figure 6

Demonstration of sequential decoding analysis (7 sorted S1 units, Dataset rat2-S1-2) in a 3 min continuous recording. (A) Sorted population spike counts. Time 0 indicates the onset of recording time. (B) Estimated mean Z-score (blue curve) from the univariate latent state variable. The vertical red lines indicate the paw withdrawal. Shaded area marks the confidence intervals. The ‘^□’ symbol denotes the false negative for significance criterion of p < 0.05.

Figure 7

Demonstration of sequential MUA decoding analysis from a S1 population (5 tetrodes, Dataset rat2-S1-2) in a 3 min continuous recording (same period as figure 6). (A) Unsorted MUA spike counts from tetrodes. Time 0 indicates the onset of recording time. (B) Estimated mean Z-score (blue curve) from the univariate latent state variable. The shaded area marks the confidence intervals. The vertical red lines indicate the paw withdrawal. The ‘^□’ symbol denotes the false negative and the ‘*’ symbol denotes the false positive for significance criterion of p < 0.05. (C ) Zoom-in duration of panel (B) between 195 and 225 s.

Figure 8

Snapshot demonstration of sequential MUA decoding analysis from an ACC population (6 tetrodes, Dataset rat5-ACC-2). (A) Unsorted MUA spike counts from tetrodes. Time 0 indicates the onset of recording time. (B) Estimated mean Z-score (blue curve) from the univariate latent state variable. The shaded area marks the confidence intervals. The vertical red lines indicate the paw withdrawal. The ‘^□’ symbol denotes the false negative and the ‘*’ symbol denotes the false positive for significance criterionof p < 0.05.

Figure 9

Sensitivity and specificity analyses. ROC curves and AUROC statistics derived from (A) Dataset rat1-S1-1 and (B) Dataset rat4-ACC-1, both based on sorted units. In the S1 example, the optimal threshold (the point closest to the upper left corner) p = 0.05 yields a true positive rate of 95% and a false positive rate 5% (average accuracy 95%). In the ACC example, the optimal threshold p = 0.05 yields a true positive rate of 100% and a false positive rate 18.8% (average accuracy 90.6%).

Figure 10

The scatter plot between the maximum (absolute value) of Z-score and the percentage of modulated units. (A) S1 dataset rat3-S1-3 (n = 30 trials). (B) ACC dataset rat6-ACC-3 (n = 32 trials). The numbers in each plot show the Spearman's rank correlation R and associated p-value.

Figure 11

Comparison of detection accuracy (true positive rate) with random subset of neurons being used (Dataset rat1-S1-1). The error bar shows the SD from 50 Monte Carlo runs.

Figure 12

Spontaneous baseline population spike activities. (A) S1 population count within two time-separated 1 min baseline periods. (B) ACC population count within two separated 1 min baseline periods. Note that there is more than a 20 min gap between those two baseline periods.

Figure 13

Impact of model (non)stationarity on pain signal detection (Dataset rat1-S1-1). ((A)–(D)) Four consecutive (second to fifth) trials. In all figures, the model parameters and noise statistics are estimated from the first single trial of S1 recording. Time 0 marks the onset of withdrawal. The horizontal dashed lines mark the significance threshold of p = 0.05. The red (solid/dashed) curve shows the inferred Z-score (mean ± CI) from an off-line EM algorithm, whereas the magenta curve shows the inferred Z-score from an online forward filtering algorithm. For clarity, the confidence intervals of the online estimate is not shown.