Relationships between correlated spikes, oxygen and LFP in the resting-state primate

Abstract

Resting-state functional MRI (rsfMRI) provides a view of human brain organization based on correlation patterns of blood oxygen level dependent (BOLD) signals recorded across the whole brain. The neural basis of resting-state BOLD fluctuations and their correlation remains poorly understood. We simultaneously recorded oxygen level, spikes, and local field potential (LFP) at multiple sites in awake, resting monkeys. Following a spike, the average local oxygen and LFP voltage responses each resemble a task-driven BOLD response, with LFP preceding oxygen by 0.5 s. Between sites, features of the long-range correlation patterns of oxygen, LFP, and spikes are similar to features seen in rsfMRI. Most of the variance shared between sites lies in the infraslow frequency band (0.01-0.1 Hz) and in the infraslow envelope of higher-frequency bands (e.g. gamma LFP). While gamma LFP and infraslow LFP are both strong correlates of local oxygen, infraslow LFP explains significantly more of the variance shared between correlated oxygen signals than any other electrophysiological signal. Together these findings are consistent with a causal relationship between infraslow LFP and long-range oxygen correlations in the resting state.

Keywords: Default mode network; Functional connectivity; Magnetic resonance imaging; Neurohemodynamic coupling; Oxygen polarography.

Fig 1.. Lagged linear correlation between oxygen and electrophysiological signals recorded from the same site.

A) Lagged correlation between oxygen and the raw LFP signal recorded in IPS (For V3 and PCC, see Fig. S6). Both the raw LFP signal and the oxygen signal were split into many frequency bands, and then correlations were calculated band by band. The X-axis denotes lag, the Y-axis denotes the frequency band and the color scale denotes correlation strength. Positive lags mean that electrophysiological signals were shifted forwards in time before computing the correlation. Correlations were most prominent at infraslow frequencies (0.01 to 0.1 Hz) with little or no lag. B) Lagged correlation between oxygen and LFP power. Format is the same as A, except that once LFP power was computed, both the LFP power signal and the oxygen signal were filtered at 0.01 to 0.1 Hz, prior to computing the correlation. Correlations were most prominent for gamma band power, with a lag of ~6 s. C) Lagged correlation between infraslow oxygen and infraslow LFP voltage (see panel A), infraslow band-limited LFP power within standard EEG bands (see panel B), and infraslow MUA. Infraslow LFP, gamma power and MUA have the strongest correlations with oxygen, significantly stronger than the next-highest correlated signal (Infraslow LFP vs. beta LFP: p < 0.05, t(58) = 2.414; gamma LFP vs. beta LFP: p < 0.05, t(58) = 3.378), but not significantly different from one another (p = 0.8, t(58) = 0.2121). Peak correlation for infraslow LFP is at 0.5 s, which is much shorter than that for gamma power (5.8 s).

Fig 2.. Spike-triggered average of oxygen, LFP voltage and gamma-band LFP.

Spikes are aligned at time zero. The top left shows a 60 s (60,000 ms) window around the spike, with expanded views in the insets. Raw LFP (blue) and oxygen (red) show a slow drop starting ~8 s before the spike. LFP has a strong negative transient around the time of the spike that lasts for ~2 s. Riding on top of this is a small (~1 uV), brief (< 20 ms) negativity almost exactly coincident with the spike (insets on right). This brief negativity likely represents contamination of the high frequency spike onto the LFP signal, while the bulk of the negativity has a duration that is too long (more than 1000 times the duration of a spike) to be explained in this way. After the spike, oxygen and raw LFP each show a delayed increase that peaks around 6 s, slowly falls back toward the baseline, and then overshoots and becomes slightly negative for ~10 s. The late slow responses in oxygen and raw LFP closely match each other, with raw LFP leading by about 0.5 s. Gamma LFP power (green) rises slowly prior to the spike, peaks abruptly at the time zero, then shows a symmetric decrease (inset on bottom left). Power is elevated from ± 8 s around time zero, though the strongest response occurs within ±1 s of the spike.

Fig 3.. Multivariate Granger causality analysis between oxygen and electrophysiological signals.

Each cell shows whether forecasts of its predicted signal (column) improve when its predictor signal (row) is included in the model alongside the rest of the signals (see Methods). A) Top, oxygen does not improve forecasts of any electrophysiological signal (first row), only infraslow LFP (LFP_INF) improves oxygen forecasts (first column), infraslow LFP forecasts are most improved by MUA (fourth column), and MUA improves forecasts of every other signal except oxygen (second row). Bottom, a graph of the Granger causality result. Arrow widths correspond to the magnitude forecast improvement, which is quantized into five categories (>0.1, 0.1–0.05, 0.05–0.04, 0.04–0.03, 0.03–0.02; for Granger causality >= 0.02, p < 0.01). Weak improvements (< 0.02) are not shown. B) When infraslow LFP is excluded from the analysis, MUA and gamma both improve oxygen forecasts (first column). Data are from V3 and PCC; see Supplemental Fig. 8 for region-specific responses.

Fig 4.. Within-mode electrophysiological and oxygen correlations.

A). Long-range correlation in raw LFP signal by frequency, within-network (red) versus across-network (blue). Correlation is present in a wide range of frequencies (0.006–200Hz). Peak correlation is around 0.05 Hz (within-network: 0.055Hz, across-network: 0.049Hz). B). Long-range correlation in LFP power. LFP power was computed at half octave bands and the power at each band was then filtered to 0.01–0.1Hz and used to compute long-range correlations. The strongest correlation was in the gamma band (55Hz). Within-network correlations were greater than across-network correlations at all frequencies. C). Like LFP, single and multi-unit activities show significant long-range, infraslow network-dependent correlations (top center and right) similar to those in oxygen (top left) and LFP (lower two rows). Correlations are significantly higher for within-network than across-network correlations in every case (oxygen: t(151) = 4.2365; single-unit: t(78) = 1.997; multi-unit: t(151) = 7.1176; infraslow LFP: t(151) = 3.2154; delta: t(151) = 2.2663; theta: t(151) = 3.6047; alpha: t(151) = 5.5918; beta: t(151) = 8.2037; gamma: t(151) = 9.2618; for single-units n = 20 within-network and n = 60 across-network; for all other signals n = 34 within-network and n =119 across-network) and are significantly lower for single units than for multiunits (note different scales). For all 9 signals shown in C, correlations are computed within the frequency range of 0.01–0.1 Hz. *** = p < 0.001; ** = p < 0.005; * = p < 0.05. Data are combined across regions (see Fig. S9).

Fig 5.. Frequency distribution of correlation and power in units and oxygen.

A). Oxygen and unit coherence as a function of frequency. Coherence provides a measure of the frequency distribution of the correlation. Both within- and across-network coherences are band-limited. This is true for oxygen, single unit, and multi-unit coherences. (Unlike LFP there was no high-pass filtering of spikes nor oxygen signals that might explain the fall-off below 0.01 Hz.) The band-limited coherences suggest that a rhythmic or near-rhythmic mechanism drives long-range, network-aligned correlation. Unlike either unit coherence or gamma BLP (Supplemental Fig. 11), whose long-range coherences are significant up to 2 Hz, oxygen coherence does not extend above 0.3 Hz. This difference is consistent with the idea that there is a low-pass stage in between neuronal activity (spikes) and the hemodynamic response. Interestingly, merely low-passing the neuronal signals, including in particular single unit activity and gamma power, still leaves us with a ~6 sec lag (Fig. 1C). In contrast, using the exact same low-pass filter on the raw LFP signal leads to a single with ~0 sec lag.. B). Power spectra of oxygen, multi-unit activity, and single unit activity. In all three cases, the power increases as frequency drops. The relationship between power and frequency is well fit by a linear relationship between the log of each signal [log (power) = -βlog(frequency) + k]. This is typically referred to a 1/f power spectrum. There are deviations from 1/f, most pronounced at around 0.01 to 0.1 Hz, which could reflect the process(es) driving the band-limited correlation. Above 10 Hz there is a plateau in spike power, which may reflect a lower limit on spike power for our recording conditions. Power above 10 Hz is excluded from the estimation of the 1/f relationship.

Fig 6.. Lagged correlation between oxygen and multi-unit activity within a site (local) and across sites separated by at least 1 cm (distal).

Black: the mean lagged correlation between oxygen and multi-unit activity recorded from the same (local) site; red: the correlation between oxygen and multi-unit activity recorded from distal sites, both in the same network; blue: the correlation between oxygen and multi-unit activity recorded from distal sites, each from different networks. Data are combined across regions; for individual regions see Fig. S12.

Fig 7.. Regression-based dependence analysis on oxygen correlation.

Left: dependence analysis determines whether the shared variance between oxygen signals is the same as the shared variance between oxygen and MUA activity (or any other electrophysiological signal). Middle: oxygen correlation before (red asterisk) and after (colored circles) regressing out linearly transformed electrophysiological signals (see Methods). Regressing out infraslow LFP (LFP_INF) had the greatest effect on oxygen correlation, substantially reducing both within- and across-network correlation (within-network: n = 27; across-network: n = 84). Right: Infraslow LFP accounts for 72% of the covariance underlying within-network oxygen correlation, and 57% of the covariance underlying across-network correlation. Gamma power accounts for 46% of within-network oxygen covariance and 37% of across-network covariance. MUA accounts for 43% of within-network oxygen covariance and 31% of across-network covariance. Other electrophysiological signals account for only 10–42% of oxygen covariance. Data are combined across regions, for individual regions see Fig. S14.