LFP and rsfMRI signals in the M1 derive highly consistent RSN spatial patterns in lightly sedated rats.

A) Simultaneous acquisition of whole-brain rsfMRI and electrophysiological signals in the M1 and ACC. B) Cross correlations between the rsfMRI signal and LFP power in the M1 (0.1-100 Hz, band interval: 1Hz, lag range: -20 – 20 s. C) Cross correlations between the rsfMRI signal and powers of individual LFP bands in the M1. Bars: SEM. D) Exemplar powers of individual LFP bands. Convolving powers of individual LFP bands with a rodent-specific hemodynamic response function (HRF) generates the corresponding LFP-predicted BOLD signals. E) M1 band-specific LFP power-derived RSN maps, obtained by voxel-wise correlating the LFP-predicted BOLD signal for each band with BOLD signals of all brain voxels. F) M1 BOLD-derived RSN map (i.e. M1 seedmap), obtained by voxel-wise correlating the regionally averaged BOLD time course of the seed (i.e. M1) with BOLD time courses of all brain voxels. G-K) Spatial similarity between the M1 BOLD-derived RSN map and the M1 LFP-derived RSN map for each band, quantified by their voxel-to-voxel correlations (G: delta, CC = -0.78; H: theta, CC = -0.78; I: alpha, CC = -0.5; J: beta, CC = -0.34; K: gamma, CC = 0.95). L) Spatial correlations between the M1 BOLD-derived RSN map and RSN maps derived by individual 1-Hz bands in the full LFP spectrum.

LFP and rsfMRI signals in the ACC derive highly consistent RSN spatial patterns in lightly sedated rats.

A) ACC band-specific LFP power-derived RSN maps, obtained by voxel-wise correlating the LFP-predicted BOLD signal for each band with BOLD signals of all brain voxels. B) ACC BOLD-derived RSN map (i.e. ACC seedmap), obtained by voxel-wise correlating the regionally averaged BOLD time course of the seed (i.e. ACC) with BOLD time courses of all brain voxels. C-G) Spatial similarity between the ACC BOLD-derived RSN map and the ACC LFP-derived RSN map for each band, quantified by their voxel-to-voxel spatial correlations (C: delta, CC = -0.62; D: theta, CC = -0.30; E: alpha, CC = -0.12; F: beta, CC = 0.12; G: gamma, CC = 0.85). H) Spatial correlations between the ACC BOLD-derived RSN map and RSN maps derived by individual 1-Hz bands in the full LFP spectrum.

Disparity in spatial and temporal correlations persists after controlling for the noise effect.

A, B) Comparison of scan-wise spatial and temporal correlations (paired t-tests across individual scans. ***:p<0.005); C-G) Simulation to evaluate factors that affect apparent correlation values including contrast-to-noise ratio (CNR) and the number of data points C) Two fixed signals with the true correlation coefficient of 0.95 are simulated (10000 data points) with random noise at a given CNR level being added. This process is repeated 159 times (i.e. # of scans in our study) for each CNR level. At each CNR, the CC was calculated either based on the averaged signals from all 159 trials (i.e. denoised data, triangle dots in D-G), or on signals of individual trials (i.e. with-noise data, round dots in D-G) before the resulting correlations are averaged across trials. D) Simulated signals resampled to 1200 data points (equal to the number of time points used to calculate temporal correlations). E) Simulated signals resampled to 6157 data points (equal to the number brain voxels used to calculate spatial correlations). Importantly, we can replicate the difference between true (R = 0.95) and apparent (R = 0.58) correlations obtained from denoised data and with-noise data, respectively, when CNR = 1.3. Therefore, we estimate that the CNR of our BOLD data is ~1.3. F,G) The same process as C-E with the true correlation of 0.59. This true correlation value is obtained by iteratively setting different true correlation values and searching for the one that provides the trial-wise apparent correlation of 0.37 (as measured by the gamma-BOLD temporal correlation in our real data, Figs. 3A) at CNR = 1.3. F) Simulated signals resampled to 1200 data points. G) Simulated signals resampled to 6157 data points.

Impact of removing the electrophysiology signal on M1 BOLD-derived RSN spatial patterns.

A) Gamma power-derived RSN map after the HRF-convolved gamma power in the M1 is regressed out from rsfMRI signals of all brain voxels. B) M1 BOLD-derived RSN map (i.e. M1 seedmap) after the HRF-convolved gamma power is voxel-wise regressed out from rsfMRI signals. C) M1 BOLD-derived RSN map after all five LFP band powers were voxel-wise regressed out from rsfMRI signals using soft regression. D) Peaks of HRF-convolved gamma power in one representative scan. E) M1 BOLD-derived RSN map after 15% rsfMRI time points corresponding to gamma peaks were removed. F) Spatial similarity of M1 BOLD-derived RSN maps before and after gamma power regression, regression of all LFP band powers or gamma peak removal.

Impact of removing the electrophysiology signal on ACC BOLD-derived RSN spatial patterns.

A) Gamma power-derived RSN map after the HRF-convolved gamma power in the ACC is regressed out from rsfMRI signals of all brain voxels. B) ACC BOLD-derived RSN map (i.e. ACC seedmap) after the HRF-convolved gamma power is voxel-wise regressed out from rsfMRI signals. C) ACC BOLD-derived RSN map after all five LFP band powers were voxel-wise regressed out from rsfMRI signals using soft regression. D) ACC BOLD-derived RSN map after 15% rsfMRI time points corresponding to gamma peaks were removed. E) Spatial similarity of ACC BOLD-derived RSN maps before and after gamma power regression, regression of all LFP band powers or gamma peak removal.

A proposed theoretic model that can explain the disparity in spatial and temporal correlations between resting-state electrophysiology and fMRI signals.

Representative T2-weighted structural images confirming the electrode location

in A) M1 and B) ACC.

Exemplar denoised LFP signal.

A) Exemplar LFP power spectrogram from one fMRI scan. B) A ‘zoom-in’ 20-sec segment of the LFP signal. Top: LFP time series; bottom: LFP power spectrogram.

Control analysis for LFP-derived spatial pattern.

A) Top: Example of gamma band power in the ACC; bottom: shuffled gamma band power. B) RSN map derived by shuffled gamma band power, obtained by voxel-wise correlating the shuffled gamma band power convolving with HRF to BOLD signals of all brain voxels.

Consistent results in the M1 without global signal regression in rsfMRI data preprocessing.

A) RSN maps derived by band-specific LFP powers in the M1. B) M1 BOLD-derived RSN map (i.e. M1 seedmap). C-G) Spatial similarity between the M1 BOLD-derived RSN map and the M1 LFP-derived RSN maps for each band, quantified by their voxel-to-voxel correlations (C: delta, CC = -0.83; D: theta, CC = -0.82; E: alpha, CC = -0.6; F: beta, CC = -0.46; G: gamma, CC = 0.94). H) Spatial correlations between the M1 BOLD-derived RSN map and RSN maps derived by all 1-Hz bands in the full LFP spectrum.

Consistent results in the ACC without global signal regression in rsfMRI data preprocessing.

A) RSN maps derived by band-specific LFP powers in the ACC. B) ACC BOLD-derived RSN map (i.e. ACC seedmap). C-G) Spatial similarity between the ACC BOLD-derived RSN map and the ACC LFP-derived RSN maps for each band, quantified by their voxel-to-voxel correlations (C: delta, CC = -0.71; D: theta, CC = -0.44; E: alpha, CC = -0.31; F: beta, CC = -0.06; G: gamma, CC = 0.88). H) Spatial correlations between the ACC BOLD-derived RSN map and RSN maps derived by all 1-Hz bands in the full LFP spectrum.

BOLD-gamma power correlations in the two-dimensional space of HRF parameters.

The maximal correlation value is close to that obtained with the HRF used in the study.

Mutual information between band-limited LFP powers and the rsfMRI signal.

Spatial correlations of M1 LFP-derived and BOLD-derived RSNs in awake animals.

A) RSN maps derived by band-specific LFP powers in the M1, obtained by voxel-wise correlating the LFP-predicted BOLD signal for each band with BOLD signals of all brain voxels. B) M1 BOLD-derived RSN map (i.e. M1 seedmap), obtained by voxel-wise correlating the regionally averaged BOLD time course of the seed (i.e. M1) with BOLD time courses of all brain voxels. C-G) Spatial similarity between the M1 BOLD-derived RSN map and M1 LFP-derived RSN maps of each band, quantified by their voxel-to-voxel correlations (C: delta, CC = -0.32; D: theta, CC = -0.41; E: alpha, CC = -0.37; F: beta, CC = -0.30; G: gamma, CC = 0.18). H) Spatial correlations between the M1 BOLD-derived RSN map and RSN maps derived by individual 1-Hz bands in the full LFP spectrum.

Spatial correlations of ACC LFP-derived and BOLD-derived RSNs in awake animals.

A) RSN maps derived by band-specific LFP powers in the ACC, obtained by voxel-wise correlating the LFP-predicted BOLD signal for each band with BOLD signals of all brain voxels. B) ACC BOLD-derived RSN map (i.e. ACC seedmap), obtained by voxel-wise correlating the regionally averaged BOLD time course of the seed (i.e. ACC) with BOLD time courses of all brain voxels. C-G) Spatial similarity between the ACC BOLD-derived RSN map and ACC LFP-derived correlation map of each band, quantified by their voxel-to-voxel correlations (C: delta, CC = -0.19; D: theta, CC = 0.03; E: alpha, CC = -0.05; F: beta, CC = 0.19; G: gamma, CC = 0.63). H) Spatial correlations between the ACC BOLD-derived RSN map and RSN maps derived by all 1-Hz bands in the full LFP spectrum.

Comparison of scan-wise spatial and temporal correlations

for A) M1 and B) ACC. Paired t tests across individual scans. # of scans = 50. ***: p<0.005.

Impact of removing the electrophysiology signal on M1 and ACC RSN spatial patterns in awake animals.

A) M1 BOLD-derived RSN map (i.e. M1 seedmap) after the HRF-convolved gamma power in the M1 is voxel-wise regressed out from rsfMRI signals of all brain voxels. B) M1 BOLD-derived RSN map after powers of all five M1 LFP bands were voxel-wise regressed out from rsfMRI signals of all brain voxels using soft regression. C) ACC BOLD-derived RSN map after the HRF-convolved gamma power in the ACC is voxel-wise regressed out from rsfMRI signals. D) ACC BOLD-derived RSN map after powers of all five ACC LFP band were voxel-wise regressed out from rsfMRI signals using soft regression. E) Spatial similarity of M1 BOLD-derived RSN map before and after gamma power regression or regression of powers of all LFP bands. F) Spatial similarity of ACC BOLD-derived RSN map before and after gamma power regression or regression of powers of all LFP bands.

Anatomical connectivity labeled by tracers.

Adopted from the database of Allen Brain Institute (Oh et al., 2014).

Schematic diagram illustrating that distal neuromodulation, which have strong vasoactive effects, can contribute to low temporal correlations between electrophysiology and rsfMRI signals.