The brain contains many cells known as neurons that send and receive messages in the form of electrical signals. The neurons in different regions of the brain must coordinate their activities to enable the brain to operate properly.

Researchers often use a method called resting-state functional magnetic resonance imaging (rsfMRI) to study how different areas of the brain work together. This method indirectly measures brain activity by detecting the changes in blood flow to different areas of the brain. Regions that are working together will become active (that is, have higher blood flow and corresponding rsfMRI signal) and inactive (have lower blood flow and a lower rsfMRI signal) at the same time. These coordinated patterns of brain activity are known as “resting-state brain networks” (RSNs).

Previous studies have identified RSNs in many different situations, but we still do not fully understand how these changes in blood flow are related to what is happening in the neurons themselves. To address this question, Tu et al. performed rsfMRI while also measuring the electrical activity (referred to as electrophysiology signals) in two distinct regions of the brains of rats. The team then used the data to generate maps of RSNs in those brain regions.

This revealed that rsfMRI signals and electrophysiology signals produced almost identical maps in terms of the locations of the RSNs. However, the electrophysiology signals only contributed a small amount to the changes in the local rsfMRI signals over time at the same recording site. This suggests that RSNs may arise from cell activities that are not detectable by electrophysiology but do regulate blood flow to neurons.

The findings of Tu et al. offer a new perspective for interpreting how rsfMRI signals relate to the activities of neurons. Further work is needed to explore all the features of the electrophysiology signals and test other methods to compare these features with rsfMRI signals in the same locations.

Sophisticated brain function requires coordinated activities from separate brain regions, collectively forming functional brain networks. Functional brain networks in humans and animals are predominantly studied using the method of resting-state functional magnetic resonance imaging (rsfMRI), which measures the synchronization of brain-wide spontaneous blood-oxygen-level-dependent (BOLD) signals. These networks are commonly referred to as resting-state brain networks (RSNs).

Despite the widespread application of BOLD-derived RSNs in both health and disease contexts, their relationship to the underlying neural activity remains incompletely understood. This is a fundamental issue, as the BOLD signal is known to indirectly reflect neural activity through accompanying hemodynamic and metabolic changes, a mechanism known as neurovascular coupling (NVC). While tight NVC has been repeatedly demonstrated when neural activities are evoked by explicit external stimulation (Goense and Logothetis, 2008; Logothetis et al., 2001; Nemoto et al., 2004), this relationship in the resting state remains elusive. There is considerable evidence indicating that the spatial patterns of most RSNs effectively mirror established functional systems and activation patterns observed during various tasks (Biswal et al., 1995; Glasser et al., 2016; Hampson et al., 2002; Lowe et al., 1998; Smith et al., 2009), as well as patterns of brain structural networks (Andrews-Hanna et al., 2010; Greicius et al., 2009; Lowe et al., 2008). In addition, alterations in RSNs have been documented in several brain disorders, aligning with neuropathophysiological changes (Buckner et al., 2009). This collective body of evidence suggests a robust neural basis for RSNs. However, it is notable that despite these findings, the predictive power of resting-state electrophysiological signals for corresponding rsfMRI time series is often relatively low (Mateo et al., 2017; Schölvinck et al., 2010; Winder et al., 2017), and some studies have even demonstrated a disconnection between neural activity and hemodynamic signals under specific conditions (Maier et al., 2008; Zhang et al., 2019). Moreover, various studies have reported divergent electrophysiological correlates of the rsfMRI signal across a broad spectrum of LFP bands, spanning from infraslow signals (Hiltunen et al., 2014; Pan et al., 2013) and low-frequency delta/sub-delta band signals (He et al., 2008; Lu et al., 2007) to high-frequency gamma-band signals (Bastos et al., 2015; Foster et al., 2015; Keller et al., 2013; Mukamel et al., 2005; Nir et al., 2008; Magri et al., 2012), as well as spiking activity (Mukamel et al., 2005). These findings suggest that the rsfMRI signal may reflect diverse aspects of neural activity. Taken together, how RSNs and rsfMRI relate to spontaneous neural activity remains unclear (Laufs et al., 2003; Liu et al., 2011; Mantini et al., 2007), highlighting a significant gap in our understanding of functional brain networks.

To address this critical issue, we systematically investigated the role of electrophysiological activity in determining specific spatiotemporal patterns of BOLD-based RSNs. In conjunction with whole-brain rsfMRI, we simultaneously recorded electrophysiology signals in the primary motor cortex (M1) and anterior cingulate cortex (ACC) in rats under light sedation and wakefulness. These brain regions were chosen due to their distinct roles in sensorimotor function and integrative cognition, respectively. Our data show that in both light-sedation and awake states, the spatial patterns of RSNs derived from gamma-band power closely resemble BOLD-derived RSNs for both the M1 and ACC, and lower-frequency band-derived RSNs exhibit inversed spatial patterns, both indicating strong neural underpinning of RSNs. However, the temporal profiles of band-limited LFP powers at both recording sites exhibit considerably lower temporal correlations with the corresponding local BOLD time courses. Moreover, regressing out the gamma-band power or powers of all LFP bands has only limited effects on the spatial patterns of BOLD-derived RSNs, collectively suggesting LFP powers contribute only partially to the local rsfMRI signal. This disparity in spatial and temporal relationships between resting-state BOLD and electrophysiology signals implies that there might be an electrophysiology-invisible component of brain activity that significantly influences the rsfMRI signal and RSNs.

To systematically analyze the spatiotemporal relationship between resting-state electrophysiology and fMRI signals, we conducted simultaneous recordings of whole-brain rsfMRI and electrophysiology signals in the M1 and ACC in rats under both light-sedation (combination of low-dose dexmedetomidine [initial bolus of 0.05 mg/kg followed by a constant infusion at the rate of 0.1 mg/kg/hr] and low-dose isoflurane [0.3%]) and awake states (Figure 1A). The accuracy of electrode placement in the M1 and ACC was confirmed using T2-weighted structural images (Figure 1—figure supplement 1). Raw electrophysiology data were initially preprocessed to remove MR artifacts using a template regression approach (Tu and Zhang, 2022). Subsequently, the LFP was extracted by bandpass filtering preprocessed electrophysiology data within the frequency range of 0.1–300 Hz. An illustration of denoised LFP is depicted in Figure 1A and Figure 1—figure supplement 2. Band-specific LFP power was computed using a conventional LFP band definition (delta: 1–4 Hz, theta: 4–7 Hz, alpha: 7–13 Hz, beta: 13–30 Hz, gamma: 40–100 Hz). Figure 1B and C illustrates the cross correlations between LFP power and BOLD signal in the M1 across the LFP spectrum (Figure 1B, 1 Hz band interval) and for individual LFP bands (Figure 1C). These data demonstrate that gamma-band power is positively correlated with the BOLD signal, while lower-frequency bands display negative peak correlations with the BOLD signal. Additionally, the lag of the BOLD signal is approximately 2 s for all bands, consistent with the hemodynamic response function (HRF) delay previously reported in rodents (Schölvinck et al., 2010; Winder et al., 2017).

Figure 1 with 5 supplements see all

Download asset Open asset

LFP and rsfMRI signals derive consistent RSN spatial patterns in lightly sedated rats

We first examined the spatial relationship between brain-wide rsfMRI signals and frequency band-specific LFP powers in lightly sedated rats. For each recording site, its BOLD-derived RSN was obtained as the seedmap, calculated by voxel-wise correlating the regionally averaged BOLD time series of the seed (M1 in Figure 1F and ACC in Figure 2B) with BOLD time series of individual brain voxels. This seedmap conventionally represents the resting-state functional connectivity (RSFC) pattern for the seed region. To assess the extent to which this RSN could be obtained using the LFP signal recorded from the same location (M1 or ACC), we convolved the power of each LFP band with a rodent-specific HRF (Figure 1D, Tong et al., 2019) to generate the LFP band-predicted BOLD signal. Subsequently, we voxel-wise correlated this signal with the brain-wide rsfMRI signal, producing the LFP band-derived RSN map (Figure 1E, Figure 2A). Our findings revealed that the gamma-band power-derived RSN map exhibited high spatial consistency with the corresponding BOLD-derived RSN map (i.e. seedmap). Specifically, for M1, the voxel-to-voxel Pearson correlation coefficient (CC) between the mean gamma-derived RSN map (Figure 1E) and mean BOLD-derived RSN map (Figure 1F) was 0.95 (Figure 1K, R²=0.90), indicating 90% of the variance in the M1 BOLD-derived RSN map could be explained by the gamma-derived map. Conversely, spatial maps generated by lower-frequency bands displayed inverse correlations with the M1 BOLD-derived RSN map, with a trend of increasingly negative spatial CC in lower-frequency bands (Figure 1E–J, delta: CC = –0.78; theta: CC = –0.78; alpha: CC = –0.5; beta: CC = –0.34), consistent with the LFP-BOLD cross correlations for these bands shown in Figure 1B and C. These relationships are repeatable in the ACC (Figure 2A–G, delta: CC = –0.62; theta: CC = –0.3; alpha: CC = –0.12; beta: CC = 0.12; gamma: CC = 0.85). These results suggest that the spatial patterns of BOLD-based RSNs can be reliably obtained using band-specific LFP signals.

Figure 2 with 3 supplements see all

Download asset Open asset

To confirm that these findings were not an artifact of specific frequency cutoffs we adopted for any LFP band, we repeated the analysis for all individual 1-Hz bands across the full LFP spectrum. Once again, we observed a gradual transition from negative to positive spatial correlations in LFP-derived RSN maps with the corresponding BOLD-derived RSN maps as the LFP signal changed from low to high frequencies (Figure 1L for M1; Figure 2H for ACC). Additionally, as a control analysis, we temporally shuffled the gamma-band power in the ACC, convolved it with the HRF, and recalculated the correlation map (Figure 2—figure supplement 1). As a result of this manipulation, the spatial pattern observed in Figure 2A disappeared, suggesting that the observed LFP-derived spatial patterns were specifically related to the LFP signal, rather than an artifact of the HRF. We also confirmed that all our results are not sensitive to the rsfMRI data preprocessing step of global signal regression (Figure 1—figure supplement 3, Figure 2—figure supplement 2).

In summary, our data collectively indicate that BOLD-derived RSNs can be reliably replicated using LFP power from the same site in lightly sedated rats, underscoring the critical involvement of neural activity in RSN spatial patterns.

Temporal correlation between LFP power and local rsfMRI signal is significant but considerably weaker

Given the high reliability of the gamma power in determining spatial patterns of BOLD-based RSNs, it is logical to expect the HRF-convolved gamma power should reliably predict the rsfMRI time series from the same location. To test this hypothesis, we calculated temporal correlations between local rsfMRI time series and HRF-convolved LFP powers for individual scans at each recording site, and then averaged the resulting correlation values across scans. Surprisingly, we found that the local rsfMRI signal exhibited considerably weaker temporal correlations with LFP powers. In the M1, the LFP-BOLD temporal correlations gradually shifted from negative to positive as the LFP signal transitioned from low to high frequencies, mirroring the trend observed in spatial correlations (Figure 1E–K). However, the absolute magnitude of these CCs was considerably lower, despite that they were all statistically significant (one-sample t-tests, delta: CC = –0.20, p=2.1 × 10^–57; theta: CC = –0.19, p=7.1 × 10^–62; alpha: CC = –0.11, p=1.2 × 10^–42; beta: CC = –0.06, p=8.4 × 10^–15; gamma: CC = 0.37; p=2.1 × 10^–58; number of scans = 159). Similar results were observed in the ACC (one-sample t-tests; delta: CC = –0.13, p=1.9 × 10^–28; theta: CC = –0.04, p=8.4 × 10^–7; alpha: CC = –0.03, p=6.0 × 10^–5; beta: CC = –0.01, p=0.1; gamma: CC = 0.18, p=6.7 × 10^–42; number of scans = 172). Additionally, we confirmed that lower temporal correlations are not due to the HRF used (Figure 3—figure supplement 1).

A notable difference in our calculation of spatial and temporal correlations may contribute to the disparity in their CC values. When computing spatial correlations, we first generated LFP- and BOLD-derived RSN maps for each scan, and then averaged these maps within each group before calculating spatial correlations using the averaged maps (Figures 1 and 2). Conversely, for temporal correlations, we initially computed for CCs for individual scans, and then averaged the resulting correlation values across scans. This approach was chosen as averaging time courses first would diminish the actual signal due to the semi-random nature of spontaneous brain activities. Consequently, the variance in averaged RSN spatial maps might be lower than the variance in time series of individual scans, which can result in higher apparent spatial correlations than temporal correlations. To control this factor, we also computed spatial correlations between LFP- and BOLD-derived RSN maps for individual scans, and then averaged the corresponding correlations across scans. Similar to scan-wise temporal correlations, scan-wise spatial correlations were significant for all LFP bands (one-sample t-tests; in the M1, delta: CC = –0.37, p=4.9 × 10^–52; theta: CC = –0.39, p=3.9 × 10^–62; alpha: CC = –0.24, p=5.8 × 10^–37; beta: CC = –0.15, p=3.9 × 10^–15; gamma: CC = 0.58, p=1.0 × 10^–56; in the ACC, delta: CC = –0.26, p=2.6 × 10^–27; theta: CC = –0.09, p=1.7 × 10^–6; alpha: CC = –0.04, p=0.04; beta: CC = 0.03, p=0.06; gamma: CC = 0.33; p=5.4 × 10^–40). Comparisons of scan-wise spatial and temporal correlations (Figure 3A and B) indicate that even after controlling for the variance level, the magnitude of spatial correlations remains appreciably higher than that of temporal correlations for all bands (paired t-tests across individual scans; in the M1, delta: p=1.01 × 10^–35; theta: p=3.74 × 10^–50; alpha: p=3.54 × 10^–25; beta: p=1.18 × 10^–12; gamma: p=7.02 × 10^–42; in the ACC, delta: p=6.73 × 10^–21; theta: p=5.75 × 10^–5; alpha: p=0.74; beta: p=7.34 × 10^–5; gamma: p=3.43 × 10^–29).

Figure 3 with 3 supplements see all

Download asset Open asset

Given that the gamma power-derived RSN map can explain ~90% of the spatial variance of the BOLD-derived RSN map (Figure 1K, R=0.95, R²=0.90) when noise is diminished by averaging RSN maps across scans, we ask how much variance of the local BOLD time series the gamma power can explain without significant influence of noise. Although we cannot directly average rsfMRI/electrophysiology time courses across scans to reduce noise levels, we can estimate the true LFP-BOLD temporal correlation by quantitatively evaluating the effect of noise on correlation values. To achieve this aim, we utilized the difference between the spatial correlation of averaged M1 RSN maps (i.e. referred to as denoised data, R=0.95, Figure 1K) and that of unaveraged RSN maps (i.e. referred to as with-noise data, R=0.58, Figure 3A, gamma-band power). Using this difference, we simulated two fixed signals with a true correlation of 0.95. By introducing varying levels of noise to the signals, we determined at what noise level the apparent correlation between the two signals became 0.58 (Figure 3C). Specifically, in each trial, random noise at a defined contrast-to-noise ratio (CNR) level was added to the simulated signals, and this process was repeated 159 times (i.e. equal to the number of scans in our study) for a given CNR level. At each CNR level, the correlation was calculated based on either the averaged signals from all 159 trials (i.e. simulating denoised data, Figure 3C), or the signals of individual trials (i.e. simulating with-noise data) before averaging resulting correlations across trials.

As anticipated, lower CNR values correspond to lower apparent trial-wise correlation values. Interestingly, we discovered that the trial-wise apparent correlation of 0.58, with the true correlation of 0.95, corresponds to the CNR of 1.3 (Figure 3D and E), which aligns with the CNR of BOLD contrast reported in the literature (Atkinson et al., 2008). At this CNR level, we estimated that the true BOLD-LFP temporal correlation in the M1 should be approximately 0.59 (R²=0.35, Figure 3F and G), when the apparent correlation is 0.37 as measured by the gamma-BOLD temporal correlation in our real data (Figure 3A). These findings indicate the temporal information provided by gamma power can only explain a minor portion (approximately 35%) of the temporal variance in the BOLD time series, even after accounting for the noise effect, which is in line with the reported correlation values between the cerebral blood volume (CBV) and fluctuations in GCaMP signal in head-fixed mice during periods of immobility (R=0.63) (Ma et al., 2016).These results are also consistent with previous reports of relatively weak temporal correlations between gamma power and hemodynamic signals at rest obtained using different imaging modalities (Schölvinck et al., 2010; Winder et al., 2017). Furthermore, our simulation suggests that the difference in the number of data points (1200 in temporal correlation calculation vs. 6157 in spatial correlation calculation) has a negligible influence on correlation values (Figure 3D–G).

Regressing out LFP powers has limited impact on RSN spatial patterns

Given the lower predictive value of LFP power on the local rsfMRI signal, we investigated the extent to which the temporal information of LFP powers affects the RSN spatial patterns. The gamma-band power in the M1 (or ACC), after convolving with HRF, was linearly regressed out from rsfMRI signals of all brain voxels. As expected, the spatial patterns of gamma power-derived RSN maps observed in Figures 1E and 2A disappeared after the regression (Figures 4A and 5A). However, this regression process minimally altered M1/ACC BOLD-derived RSN maps (Figures 4B and 5B). This result remained consistent when the powers of all LFP bands were voxel-wise regressed out from rsfMRI signals using soft regression (Figures 4C and 5C). Soft regression was utilized to address the multicollinearity issue in the regression model resulting from potential correlations between LFP bands, as this method allows only unique components in five LFP bands to be regressed out.

Figure 4 with 1 supplement see all

Download asset Open asset

Figure 5 with 1 supplement see all

Download asset Open asset

To investigate whether the regression process is disproportionately dominated by time points with the largest LFP amplitude (i.e. outliers), we recalculated the M1/ACC BOLD-derived RSN maps after removing rsfMRI volumes corresponding to peaks in the M1/ACC gamma power (i.e. time points with the signal amplitude above the 85th percentile in HRF-convolved gamma power of each scan, Figures 4D, E, 5D). The spatial similarities between the BOLD-derived RSN maps before and after gamma power regression, all band power regression, or peak removal are summarized in Figures 4F and 5E, showing that the removal of gamma power has limited impact on the M1/ACC RSN maps.

To control for the potential nonlinear relationship between band-specific LFP powers and the rsfMRI signal, we calculated the mutual information between band-limited LFP powers and rsfMRI signals for all brain voxels (Figure 5—figure supplement 1). The results show limited mutual information between any band-specific power and voxel-wise rsfMRI signals, indicating that the nonlinear component between BOLD and electrophysiological signals, reflected by mutual information, does not significantly influence RSN spatial patterns, which is consistent with the report that macroscopic resting-state brain dynamics are best described by linear models (Nozari et al., 2024). These data collectively indicate that the temporal fluctuations of LFP have limited effects on BOLD-derived RSN spatial patterns.

Ongoing rsfMRI signal could be contributed by electrophysiology-invisible brain activities

Our findings indicate that the LFP signal can capture RSN patterns that account for nearly all the spatial variance observed in BOLD-based seedmaps. However, the temporal dynamics of the LFP signal only explain a minor fraction of the local BOLD time series and have minimal impact on the spatial patterns of BOLD-based RSNs. To reconcile this apparent contradiction, we propose a theoretical model, described as follows.

Brain activity consists of components measurable by electrophysiology and others that are electrophysiology-invisible. In addition to the electrophysiology activity, electrophysiology-invisible brain activities, such as those involving nNOS neurons and astrocytes, actively contribute to NVC and can exert a significant influence on the rsfMRI signal. During the resting state, these two components may not be synchronized, lowering temporal correlations between electrophysiology and rsfMRI signals. Another factor contributing to low LFP-BOLD temporal correlations could be neuromodulations from distal modulator nuclei (e.g. locus coerulues and/or basal forebrain), which exert strong vasoactive effects but may not proportionately affect electrophysiology activity. Moreover, the signaling of electrophysiology activities and that of electrophysiology-invisible are both constrained by the same anatomical pathways. This allows the two signals to generate similar RSN spatial patterns in parallel measured by the BOLD signal, which can reflect both direct and indirect anatomical connectivity. The model is summarized in Figure 6 and Figure 6—figure supplement 1, and further details will be discussed in the next section.

Figure 6 with 1 supplement see all

Download asset Open asset

BOLD-derived RSNs have been widely investigated in multiple species (Damoiseaux et al., 2006; Lu et al., 2012; Mantini et al., 2011; Liang et al., 2011) and have been applied in various physiological and pathological conditions. However, the neural mechanisms underlying RSNs and the rsfMRI signal remain incompletely understood. To shed more light on this issue, we conducted systematic analysis of simultaneously recorded electrophysiology and rsfMRI signals in both lightly anesthetized and awake animals. Our findings reveal that both electrophysiology and rsfMRI signals can generate highly consistent brain-wide RSN patterns. However, the temporal information of the LFP signal contributes only minimally to the local BOLD time series at the same recording site. These seemingly paradoxical findings, along with those reported in the literature, can potentially be reconciled by the theoretical model we propose, which suggests that RSNs may also arise from electrophysiology-invisible brain activities that play a significant role in NVC. Together, our data and model offer a new perspective for interpreting the neural basis underlying the resting-state BOLD signal.

The spatial correspondence between BOLD- and electrophysiology-derived RSNs has been repeatedly reported across various physiological states and species using different methods. Studies employing electroencephalography or electrocorticography in humans have shown that RSNs derived from the power of multiple-site electrophysiological signals exhibit similar spatial patterns to classic BOLD-derived RSNs, such as the default-mode network (Hacker et al., 2017; Kucyi et al., 2018). This high spatial correspondence between rsfMRI and LFP signals can even be found at the columnar level (Shi et al., 2017). Similarly, simultaneous recordings of resting-state calcium and fMRI signals in awake rats have revealed highly consistent spatial patterns between calcium- and BOLD-associated RSNs (Ma et al., 2022). Moreover, voltage-sensitive dye imaging in mice has unveiled comparable sensory-evoked and hemisphere-wide activity motifs represented in spontaneous activity in both lightly anesthetized and awake states (Mohajerani et al., 2013). Furthermore, in a more recent study by Vafaii and colleagues, overlapping cortical networks were identified using both fMRI and calcium imaging modalities, suggesting that networks observable in fMRI studies exhibit corresponding neural activity spatial patterns (Vafaii et al., 2024). These results align well with the notion that RSN spatial patterns are highly consistent with known functional systems and activation patterns observed in task-based studies (Biswal et al., 1995; Glasser et al., 2016; Hampson et al., 2002; Lowe et al., 1998; Smith et al., 2009), as well as patterns of structural networks (Andrews-Hanna et al., 2010; Greicius et al., 2009; Lowe et al., 2008). Taken together, previous studies and our data indicate that both electrophysiology and rsfMRI measurements can generate consistent RSN spatial patterns, strongly suggesting that the spatial structures of RSNs are dictated by neural activities.

Previous studies have also indicated that RSNs are likely constrained by axonal projections. For instance, consistent sensory-evoked and hemisphere-wide activity motifs in mice, as revealed using voltage-sensitive dye imaging, are defined by regional axonal projections (Mohajerani et al., 2013). Our previous work in awake rats further demonstrate spatial consistency between RSNs and anatomical connectivity patterns in thalamocortical networks (Liang et al., 2013). In the current study, we compared RSNs of M1 and ACC to their anatomical networks defined by the axonal projection patterns obtained from the Allen Brain Institute database (Figure 1—figure supplement 5, Oh et al., 2014), illustrating that RSNs revealed by either the BOLD or LFP signal closely resemble the corresponding anatomical networks. It is important to note that RSNs measured by functional connectivity can reflect both direct and indirect connectivity and thus may not necessarily have identical spatial patterns as the corresponding anatomical networks (Honey et al., 2009).

In contrast to the seemingly strong LFP-BOLD relationship inferred from their high spatial correlations, we observed appreciably lower (yet significant) temporal correlations between the two signals from the same recording sites (M1 and ACC). Regressing out LFP powers has limited impact on RSN spatial patterns, reinforcing the notion that the contribution of temporal variations of the LFP signal to RSN spatial patterns is minor. These results are supported by previous research demonstrating weak but significant correlations between CBV changes and spontaneous gamma-band LFP or multiunit activity in awake, head-fixed mice (Winder et al., 2017). Importantly, persistent spontaneous fluctuations in CBV were observed even after blocking local neural spiking and glutamatergic input, as well as noradrenergic receptors, indicating that hemodynamic signal fluctuations may not dominantly reflect local ongoing electrophysiology activity (Winder et al., 2017). Comparably low temporal correlations between gamma-band power and the rsfMRI signal have been reported in monkeys (Schölvinck et al., 2010; Shmuel and Leopold, 2008). Additionally, data from our group show similarly low temporal correlations between spontaneous calcium peaks, a measure of neural spiking activity, and the rsfMRI signal in awake rats (Ma et al., 2022). Furthermore, Vafaii et al. revealed notable differences in functional connectivity strength measured by fMRI and calcium imaging, despite an overlapping spatial pattern of cortical networks identified by both modalities (Vafaii et al., 2024). These findings collectively suggest that while the temporal correlation between electrophysiology and rsfMRI signals is significant, the effect size of this correlation might be small. This result appears to remain consistent even for infraslow LFP activity (<1 Hz). Our data show that in the M1, the temporal correlation between infraslow LFP power and the rsfMRI signal was 0.08, while both derived consistent RSN spatial patterns (spatial correlation = 0.7), in line with the report that RSNs can be derived from infraslow LFP activity (Li et al., 2022). It is noteworthy that these results differ from two earlier studies in isoflurane-anesthetized rats, which found the LFP power in the primary somatosensory cortex was highly correlated with BOLD fluctuations (Liu et al., 2011; Pan et al., 2011). This discrepancy may be attributed to brain-wide synchronization during burst suppression in deeply anesthetized states in those studies.

Why would electrophysiology and rsfMRI signals exhibit unmatched spatial and temporal correlations? Our model offers one possible scenario that can reconcile this discrepancy. It hypothesizes that a portion of the rsfMRI signal is driven by electrophysiology-invisible brain activities involved in NVC. LFP records various neural activities, such as synaptic potentials and voltage-gated membrane fluctuations, reflecting the input and local neural processing of a particular brain region. However, electrophysiology cannot measure activities from certain cell populations, while electrophysiology-invisible components can trigger vasoactive responses and significantly contribute to the rsfMRI signal. For instance, the electrical activity of nNOS neurons is not detectable by electrophysiology because the nNOS neuron population is very small, yet it strongly contributes to NVC. Chemogenetic or pharmacological stimulation of nNOS neurons causes vasodilation without detectable changes in LFP (Echagarruga et al., 2020). In addition, astrocytes, a type of glia cell, coordinate communication between neurons and blood vessels and play a crucial role in NVC. Astrocytes regulate vessel tone by releasing signaling molecules such as ATP, arachidonic acid metabolites, and nitric oxide (Iadecola and Nedergaard, 2007; Mulligan and MacVicar, 2004; Murphy et al., 1993). Furthermore, astrocytes can alter the diameter of blood vessels by extending or retracting the endfeet wrapping around them (Mills et al., 2022; Niu et al., 2019). Optogenetic stimulation of astrocytes in transgenic mice without affecting neurons elicited a BOLD response, indicating that astrocyte activity alone can cause changes in the BOLD signal, independent of neuronal activity (Takata et al., 2018). Additionally, Uhlirova et al. conducted a study where they utilized optogenetic stimulation and two-photon imaging to investigate how the activation of different neuron types affects blood vessels in mice. They discovered that only the activation of inhibitory neurons led to vessel constriction, albeit with a negligible impact on LFP (Uhlirova et al., 2016). These studies collectively suggest electrophysiology-invisible activities can significantly drive the rsfMRI signal. Exclusively identifying all electrophysiology-invisible sources contributing to the rsfMRI signal is beyond the scope of this work. However, a key point is that as the resting-state LFP and electrophysiology-invisible (and thus rsfMRI) signals reflect different components of brain activities, they can be minimally synchronized and display low temporal correlations. Another possible factor that can contribute to low BOLD-LFP temporal correlations is direct modulation of the vasculature from distant modulator nuclei (e.g. locus coerulues and/or basal forebrain). Some neuromodulators, such as norepinephrine (Bekar et al., 2012; Kim et al., 2016) and acetylcholine (Sato and Sato, 1995), have vasoconstrictive/vasodilatory effects that are spatially targeted in distributed brain regions, and thus can modulate brain-wide rsfMRI signals. However, these neural modulation effects may not be proportionately reflected from the electrophysiology signal, lowering BOLD-LFP temporal correlations (see a schematic diagram in Figure 6—figure supplement 1). On the other hand, as the signaling of LFP- and electrophysiology-invisible components in functional networks is constrained by the same anatomical connectivity structure (Liu et al., 2018b; Liu et al., 2018a), the LFP- and BOLD-derived RSN spatial patterns can be highly similar and thus have high spatial correlations.

Our model can also potentially explain high spatial and temporal correlations when brain activation is evoked by external stimulation (Winder et al., 2017). At the evoked state, both LFP (and spiking activity) and electrophysiology-invisible components are temporally modulated by the same external stimulation paradigm, leading to both high temporal and high spatial correlations between fMRI and electrophysiology signals.

All preprocessed electrophysiology and fMRI data are deposited at https://zenodo.org/records/10537319.

1. 1. Tu W
   2. Cramer SR
   3. Zhang N

   (2024) Zenodo

   Resting state fMRI and electrophysiology in rats.

   https://doi.org/10.5281/zenodo.10537319

1. 1. Andrews-Hanna JR
   2. Reidler JS
   3. Sepulcre J
   4. Poulin R
   5. Buckner RL

   (2010) Functional-anatomic fractionation of the brain’s default network

   Neuron 65:550–562.

   https://doi.org/10.1016/j.neuron.2010.02.005

   • PubMed
   • Google Scholar
2. 1. Atkinson IC
   2. Kamalabadi F
   3. Jones DL
   4. Thulborn KR

   (2008) Blind estimation for localized low contrast-to-noise ratio BOLD signals

   IEEE Journal of Selected Topics in Signal Processing 2:879–890.

   https://doi.org/10.1109/JSTSP.2008.2007763

   • Google Scholar
3. 1. Bastos AM
   2. Vezoli J
   3. Bosman CA
   4. Schoffelen J-M
   5. Oostenveld R
   6. Dowdall JR
   7. De Weerd P
   8. Kennedy H
   9. Fries P

   (2015) Visual areas exert feedforward and feedback influences through distinct frequency channels

   Neuron 85:390–401.

   https://doi.org/10.1016/j.neuron.2014.12.018

   • PubMed
   • Google Scholar
4. 1. Bekar LK
   2. Wei HS
   3. Nedergaard M

   (2012) The locus coeruleus-norepinephrine network optimizes coupling of cerebral blood volume with oxygen demand

   Journal of Cerebral Blood Flow and Metabolism 32:2135–2145.

   https://doi.org/10.1038/jcbfm.2012.115

   • PubMed
   • Google Scholar
5. 1. Bergmann E
   2. Zur G
   3. Bershadsky G
   4. Kahn I

   (2016) The organization of mouse and human cortico-hippocampal networks estimated by intrinsic functional connectivity

   Cerebral Cortex 26:4497–4512.

   https://doi.org/10.1093/cercor/bhw327

   • PubMed
   • Google Scholar
6. 1. Biswal B
   2. Yetkin FZ
   3. Haughton VM
   4. Hyde JS

   (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI

   Magnetic Resonance in Medicine 34:537–541.

   https://doi.org/10.1002/mrm.1910340409

   • PubMed
   • Google Scholar
7. 1. Bolt T
   2. Nomi JS
   3. Bzdok D
   4. Salas JA
   5. Chang C
   6. Thomas Yeo BT
   7. Uddin LQ
   8. Keilholz SD

   (2022) A parsimonious description of global functional brain organization in three spatiotemporal patterns

   Nature Neuroscience 25:1093–1103.

   https://doi.org/10.1038/s41593-022-01118-1

   • PubMed
   • Google Scholar
8. 1. Buckner RL
   2. Sepulcre J
   3. Talukdar T
   4. Krienen FM
   5. Liu H
   6. Hedden T
   7. Andrews-Hanna JR
   8. Sperling RA
   9. Johnson KA

   (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease

   The Journal of Neuroscience 29:1860–1873.

   https://doi.org/10.1523/JNEUROSCI.5062-08.2009

   • PubMed
   • Google Scholar
9. 1. Cabral J
   2. Fernandes FF
   3. Shemesh N

   (2023) Intrinsic macroscale oscillatory modes driving long range functional connectivity in female rat brains detected by ultrafast fMRI

   Nature Communications 14:375.

   https://doi.org/10.1038/s41467-023-36025-x

   • PubMed
   • Google Scholar
10. 1. Chang PC
    2. Procissi D
    3. Bao Q
    4. Centeno MV
    5. Baria A
    6. Apkarian AV

    (2016) Novel method for functional brain imaging in awake minimally restrained rats

    Journal of Neurophysiology 116:61–80.

    https://doi.org/10.1152/jn.01078.2015

    • PubMed
    • Google Scholar
11. 1. Damoiseaux JS
    2. Rombouts SARB
    3. Barkhof F
    4. Scheltens P
    5. Stam CJ
    6. Smith SM
    7. Beckmann CF

    (2006) Consistent resting-state networks across healthy subjects

    PNAS 103:13848–13853.

    https://doi.org/10.1073/pnas.0601417103

    • PubMed
    • Google Scholar
12. 1. Echagarruga CT
    2. Gheres KW
    3. Norwood JN
    4. Drew PJ

    (2020) nNOS-expressing interneurons control basal and behaviorally evoked arterial dilation in somatosensory cortex of mice

    eLife 9:e60533.

    https://doi.org/10.7554/eLife.60533

    • PubMed
    • Google Scholar
13. 1. Foster BL
    2. Rangarajan V
    3. Shirer WR
    4. Parvizi J

    (2015) Intrinsic and task-dependent coupling of neuronal population activity in human parietal cortex

    Neuron 86:578–590.

    https://doi.org/10.1016/j.neuron.2015.03.018

    • PubMed
    • Google Scholar
14. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
15. 1. Goense JBM
    2. Logothetis NK

    (2008) Neurophysiology of the BOLD fMRI signal in awake monkeys

    Current Biology 18:631–640.

    https://doi.org/10.1016/j.cub.2008.03.054

    • PubMed
    • Google Scholar
16. 1. Greicius MD
    2. Supekar K
    3. Menon V
    4. Dougherty RF

    (2009) Resting-state functional connectivity reflects structural connectivity in the default mode network

    Cerebral Cortex 19:72–78.

    https://doi.org/10.1093/cercor/bhn059

    • PubMed
    • Google Scholar
17. 1. Griffanti L
    2. Salimi-Khorshidi G
    3. Beckmann CF
    4. Auerbach EJ
    5. Douaud G
    6. Sexton CE
    7. Zsoldos E
    8. Ebmeier KP
    9. Filippini N
    10. Mackay CE
    11. Moeller S
    12. Xu J
    13. Yacoub E
    14. Baselli G
    15. Ugurbil K
    16. Miller KL
    17. Smith SM

    (2014) ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

    NeuroImage 95:232–247.

    https://doi.org/10.1016/j.neuroimage.2014.03.034

    • PubMed
    • Google Scholar
18. 1. Hacker CD
    2. Snyder AZ
    3. Pahwa M
    4. Corbetta M
    5. Leuthardt EC

    (2017) Frequency-specific electrophysiologic correlates of resting state fMRI networks

    NeuroImage 149:446–457.

    https://doi.org/10.1016/j.neuroimage.2017.01.054

    • PubMed
    • Google Scholar
19. 1. Hampson M
    2. Peterson BS
    3. Skudlarski P
    4. Gatenby JC
    5. Gore JC

    (2002) Detection of functional connectivity using temporal correlations in MR images

    Human Brain Mapping 15:247–262.

    https://doi.org/10.1002/hbm.10022

    • PubMed
    • Google Scholar
20. 1. He BJ
    2. Snyder AZ
    3. Zempel JM
    4. Smyth MD
    5. Raichle ME

    (2008) Electrophysiological correlates of the brain’s intrinsic large-scale functional architecture

    PNAS 105:16039–16044.

    https://doi.org/10.1073/pnas.0807010105

    • PubMed
    • Google Scholar
21. 1. Hiltunen T
    2. Kantola J
    3. Abou Elseoud A
    4. Lepola P
    5. Suominen K
    6. Starck T
    7. Nikkinen J
    8. Remes J
    9. Tervonen O
    10. Palva S
    11. Kiviniemi V
    12. Palva JM

    (2014) Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI

    The Journal of Neuroscience 34:356–362.

    https://doi.org/10.1523/JNEUROSCI.0276-13.2014

    • PubMed
    • Google Scholar
22. 1. Honey CJ
    2. Sporns O
    3. Cammoun L
    4. Gigandet X
    5. Thiran JP
    6. Meuli R
    7. Hagmann P

    (2009) Predicting human resting-state functional connectivity from structural connectivity

    PNAS 106:2035–2040.

    https://doi.org/10.1073/pnas.0811168106

    • PubMed
    • Google Scholar
23. 1. Iadecola C
    2. Nedergaard M

    (2007) Glial regulation of the cerebral microvasculature

    Nature Neuroscience 10:1369–1376.

    https://doi.org/10.1038/nn2003

    • PubMed
    • Google Scholar
24. 1. Keller CJ
    2. Bickel S
    3. Honey CJ
    4. Groppe DM
    5. Entz L
    6. Craddock RC
    7. Lado FA
    8. Kelly C
    9. Milham M
    10. Mehta AD

    (2013) Neurophysiological investigation of spontaneous correlated and anticorrelated fluctuations of the BOLD signal

    The Journal of Neuroscience 33:6333–6342.

    https://doi.org/10.1523/JNEUROSCI.4837-12.2013

    • PubMed
    • Google Scholar
25. 1. Kim JH
    2. Jung AH
    3. Jeong D
    4. Choi I
    5. Kim K
    6. Shin S
    7. Kim SJ
    8. Lee SH

    (2016) Selectivity of neuromodulatory projections from the basal forebrain and locus ceruleus to primary sensory cortices

    The Journal of Neuroscience 36:5314–5327.

    https://doi.org/10.1523/JNEUROSCI.4333-15.2016

    • PubMed
    • Google Scholar
26. 1. Kucyi A
    2. Schrouff J
    3. Bickel S
    4. Foster BL
    5. Shine JM
    6. Parvizi J

    (2018) Intracranial electrophysiology reveals reproducible intrinsic functional connectivity within human brain networks

    The Journal of Neuroscience 38:4230–4242.

    https://doi.org/10.1523/JNEUROSCI.0217-18.2018

    • PubMed
    • Google Scholar
27. 1. Laufs H
    2. Kleinschmidt A
    3. Beyerle A
    4. Eger E
    5. Salek-Haddadi A
    6. Preibisch C
    7. Krakow K

    (2003) EEG-correlated fMRI of human alpha activity

    NeuroImage 19:1463–1476.

    https://doi.org/10.1016/s1053-8119(03)00286-6

    • PubMed
    • Google Scholar
28. 1. Li JM
    2. Acland BT
    3. Brenner AS
    4. Bentley WJ
    5. Snyder LH

    (2022) Relationships between correlated spikes, oxygen and LFP in the resting-state primate

    NeuroImage 247:118728.

    https://doi.org/10.1016/j.neuroimage.2021.118728

    • PubMed
    • Google Scholar
29. 1. Liang Z
    2. King J
    3. Zhang N

    (2011) Uncovering intrinsic connectional architecture of functional networks in awake rat brain

    The Journal of Neuroscience 31:3776–3783.

    https://doi.org/10.1523/JNEUROSCI.4557-10.2011

    • PubMed
    • Google Scholar
30. 1. Liang Z
    2. King J
    3. Zhang N

    (2012) Intrinsic organization of the anesthetized brain

    The Journal of Neuroscience 32:10183–10191.

    https://doi.org/10.1523/JNEUROSCI.1020-12.2012

    • PubMed
    • Google Scholar
31. 1. Liang Z
    2. Li T
    3. King J
    4. Zhang N

    (2013) Mapping thalamocortical networks in rat brain using resting-state functional connectivity

    NeuroImage 83:237–244.

    https://doi.org/10.1016/j.neuroimage.2013.06.029

    • PubMed
    • Google Scholar
32. 1. Liu X
    2. Zhu XH
    3. Zhang Y
    4. Chen W

    (2011) Neural origin of spontaneous hemodynamic fluctuations in rats under burst-suppression anesthesia condition

    Cerebral Cortex 21:374–384.

    https://doi.org/10.1093/cercor/bhq105

    • PubMed
    • Google Scholar
33. 1. Liu X
    2. de Zwart JA
    3. Schölvinck ML
    4. Chang C
    5. Ye FQ
    6. Leopold DA
    7. Duyn JH

    (2018a) Subcortical evidence for a contribution of arousal to fMRI studies of brain activity

    Nature Communications 9:395.

    https://doi.org/10.1038/s41467-017-02815-3

    • PubMed
    • Google Scholar
34. 1. Liu M
    2. Zhang J
    3. Nie D
    4. Yap PT
    5. Shen D

    (2018b) Anatomical landmark based deep feature representation for MR images in brain disease diagnosis

    IEEE Journal of Biomedical and Health Informatics 22:1476–1485.

    https://doi.org/10.1109/JBHI.2018.2791863

    • PubMed
    • Google Scholar
35. 1. Logothetis NK
    2. Pauls J
    3. Augath M
    4. Trinath T
    5. Oeltermann A

    (2001) Neurophysiological investigation of the basis of the fMRI signal

    Nature 412:150–157.

    https://doi.org/10.1038/35084005

    • PubMed
    • Google Scholar
36. 1. Lowe MJ
    2. Mock BJ
    3. Sorenson JA

    (1998) Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations

    NeuroImage 7:119–132.

    https://doi.org/10.1006/nimg.1997.0315

    • PubMed
    • Google Scholar
37. 1. Lowe MJ
    2. Beall EB
    3. Sakaie KE
    4. Koenig KA
    5. Stone L
    6. Marrie RA
    7. Phillips MD

    (2008) Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity

    Human Brain Mapping 29:818–827.

    https://doi.org/10.1002/hbm.20576

    • PubMed
    • Google Scholar
38. 1. Lu H
    2. Zuo Y
    3. Gu H
    4. Waltz JA
    5. Zhan W
    6. Scholl CA
    7. Rea W
    8. Yang Y
    9. Stein EA

    (2007) Synchronized delta oscillations correlate with the resting-state functional MRI signal

    PNAS 104:18265–18269.

    https://doi.org/10.1073/pnas.0705791104

    • Google Scholar
39. 1. Lu H
    2. Zou Q
    3. Gu H
    4. Raichle ME
    5. Stein EA
    6. Yang Y

    (2012) Rat brains also have a default mode network

    PNAS 109:3979–3984.

    https://doi.org/10.1073/pnas.1200506109

    • PubMed
    • Google Scholar
40. 1. Lu H
    2. Wang L
    3. Rea WW
    4. Brynildsen JK
    5. Jaime S
    6. Zuo Y
    7. Stein EA
    8. Yang Y

    (2016) Low- but not high-frequency LFP correlates with spontaneous BOLD fluctuations in rat whisker barrel Cortex

    Cerebral Cortex 26:683–694.

    https://doi.org/10.1093/cercor/bhu248

    • PubMed
    • Google Scholar
41. 1. Ma Y
    2. Shaik MA
    3. Kozberg MG
    4. Kim SH
    5. Portes JP
    6. Timerman D
    7. Hillman EMC

    (2016) Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons

    PNAS 113:E8463–E8471.

    https://doi.org/10.1073/pnas.1525369113

    • PubMed
    • Google Scholar
42. 1. Ma Z
    2. Zhang N

    (2018) Temporal transitions of spontaneous brain activity

    eLife 7:e33562.

    https://doi.org/10.7554/eLife.33562

    • PubMed
    • Google Scholar
43. 1. Ma Z
    2. Zhang Q
    3. Tu W
    4. Zhang N

    (2022) Gaining insight into the neural basis of resting-state fMRI signal

    NeuroImage 250:118960.

    https://doi.org/10.1016/j.neuroimage.2022.118960

    • PubMed
    • Google Scholar
44. 1. Magri C
    2. Schridde U
    3. Murayama Y
    4. Panzeri S
    5. Logothetis NK

    (2012) The amplitude and timing of the BOLD signal reflects the relationship between local field potential power at different frequencies

    The Journal of Neuroscience 32:1395–1407.

    https://doi.org/10.1523/JNEUROSCI.3985-11.2012

    • PubMed
    • Google Scholar
45. 1. Maier A
    2. Wilke M
    3. Aura C
    4. Zhu C
    5. Ye FQ
    6. Leopold DA

    (2008) Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey

    Nature Neuroscience 11:1193–1200.

    https://doi.org/10.1038/nn.2173

    • PubMed
    • Google Scholar
46. 1. Mantini D
    2. Perrucci MG
    3. Del Gratta C
    4. Romani GL
    5. Corbetta M

    (2007) Electrophysiological signatures of resting state networks in the human brain

    PNAS 104:13170–13175.

    https://doi.org/10.1073/pnas.0700668104

    • PubMed
    • Google Scholar
47. 1. Mantini D
    2. Gerits A
    3. Nelissen K
    4. Durand JB
    5. Joly O
    6. Simone L
    7. Sawamura H
    8. Wardak C
    9. Orban GA
    10. Buckner RL
    11. Vanduffel W

    (2011) Default mode of brain function in monkeys

    The Journal of Neuroscience 31:12954–12962.

    https://doi.org/10.1523/JNEUROSCI.2318-11.2011

    • PubMed
    • Google Scholar
48. 1. Mateo C
    2. Knutsen PM
    3. Tsai PS
    4. Shih AY
    5. Kleinfeld D

    (2017) Entrainment of arteriole vasomotor fluctuations by neural activity is a basis of blood-oxygenation-level-dependent “resting-state” connectivity

    Neuron 96:936–948.

    https://doi.org/10.1016/j.neuron.2017.10.012

    • PubMed
    • Google Scholar
49. 1. Mills WA
    2. Woo AM
    3. Jiang S
    4. Martin J
    5. Surendran D
    6. Bergstresser M
    7. Kimbrough IF
    8. Eyo UB
    9. Sofroniew MV
    10. Sontheimer H

    (2022) Astrocyte plasticity in mice ensures continued endfoot coverage of cerebral blood vessels following injury and declines with age

    Nature Communications 13:1794.

    https://doi.org/10.1038/s41467-022-29475-2

    • PubMed
    • Google Scholar
50. 1. Mohajerani MH
    2. Chan AW
    3. Mohsenvand M
    4. LeDue J
    5. Liu R
    6. McVea DA
    7. Boyd JD
    8. Wang YT
    9. Reimers M
    10. Murphy TH

    (2013) Spontaneous cortical activity alternates between motifs defined by regional axonal projections

    Nature Neuroscience 16:1426–1435.

    https://doi.org/10.1038/nn.3499

    • PubMed
    • Google Scholar
51. 1. Mukamel R
    2. Gelbard H
    3. Arieli A
    4. Hasson U
    5. Fried I
    6. Malach R

    (2005) Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex

    Science 309:951–954.

    https://doi.org/10.1126/science.1110913

    • PubMed
    • Google Scholar
52. 1. Mulligan SJ
    2. MacVicar BA

    (2004) Calcium transients in astrocyte endfeet cause cerebrovascular constrictions

    Nature 431:195–199.

    https://doi.org/10.1038/nature02827

    • PubMed
    • Google Scholar
53. 1. Murphy S
    2. Simmons ML
    3. Agullo L
    4. Garcia A
    5. Feinstein DL
    6. Galea E
    7. Reis DJ
    8. Minc-Golomb D
    9. Schwartz JP

    (1993) Synthesis of nitric oxide in CNS glial cells

    Trends in Neurosciences 16:323–328.

    https://doi.org/10.1016/0166-2236(93)90109-Y

    • Google Scholar
54. 1. Nemoto M
    2. Sheth S
    3. Guiou M
    4. Pouratian N
    5. Chen JWY
    6. Toga AW

    (2004) Functional signal- and paradigm-dependent linear relationships between synaptic activity and hemodynamic responses in rat somatosensory cortex

    The Journal of Neuroscience 24:3850–3861.

    https://doi.org/10.1523/JNEUROSCI.4870-03.2004

    • PubMed
    • Google Scholar
55. 1. Nir Y
    2. Mukamel R
    3. Dinstein I
    4. Privman E
    5. Harel M
    6. Fisch L
    7. Gelbard-Sagiv H
    8. Kipervasser S
    9. Andelman F
    10. Neufeld MY
    11. Kramer U
    12. Arieli A
    13. Fried I
    14. Malach R

    (2008) Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex

    Nature Neuroscience 11:1100–1108.

    https://doi.org/10.1038/nn.2177

    • PubMed
    • Google Scholar
56. 1. Niu J
    2. Tsai HH
    3. Hoi KK
    4. Huang N
    5. Yu G
    6. Kim K
    7. Baranzini SE
    8. Xiao L
    9. Chan JR
    10. Fancy SPJ

    (2019) Aberrant oligodendroglial-vascular interactions disrupt the blood-brain barrier, triggering CNS inflammation

    Nature Neuroscience 22:709–718.

    https://doi.org/10.1038/s41593-019-0369-4

    • PubMed
    • Google Scholar
57. 1. Nozari E
    2. Bertolero MA
    3. Stiso J
    4. Caciagli L
    5. Cornblath EJ
    6. He X
    7. Mahadevan AS
    8. Pappas GJ
    9. Bassett DS

    (2024) Macroscopic resting-state brain dynamics are best described by linear models

    Nature Biomedical Engineering 8:68–84.

    https://doi.org/10.1038/s41551-023-01117-y

    • PubMed
    • Google Scholar
58. 1. Oh SW
    2. Harris JA
    3. Ng L
    4. Winslow B
    5. Cain N
    6. Mihalas S
    7. Wang Q
    8. Lau C
    9. Kuan L
    10. Henry AM
    11. Mortrud MT
    12. Ouellette B
    13. Nguyen TN
    14. Sorensen SA
    15. Slaughterbeck CR
    16. Wakeman W
    17. Li Y
    18. Feng D
    19. Ho A
    20. Nicholas E
    21. Hirokawa KE
    22. Bohn P
    23. Joines KM
    24. Peng H
    25. Hawrylycz MJ
    26. Phillips JW
    27. Hohmann JG
    28. Wohnoutka P
    29. Gerfen CR
    30. Koch C
    31. Bernard A
    32. Dang C
    33. Jones AR
    34. Zeng H

    (2014) A mesoscale connectome of the mouse brain

    Nature 508:207–214.

    https://doi.org/10.1038/nature13186

    • PubMed
    • Google Scholar
59. 1. Pan WJ
    2. Thompson G
    3. Magnuson M
    4. Majeed W
    5. Jaeger D
    6. Keilholz S

    (2011) Broadband local field potentials correlate with spontaneous fluctuations in functional magnetic resonance imaging signals in the rat somatosensory cortex under isoflurane anesthesia

    Brain Connectivity 1:119–131.

    https://doi.org/10.1089/brain.2011.0014

    • PubMed
    • Google Scholar
60. 1. Pan WJ
    2. Thompson GJ
    3. Magnuson ME
    4. Jaeger D
    5. Keilholz S

    (2013) Infraslow LFP correlates to resting-state fMRI BOLD signals

    NeuroImage 74:288–297.

    https://doi.org/10.1016/j.neuroimage.2013.02.035

    • PubMed
    • Google Scholar
61. 1. Sato A
    2. Sato Y

    (1995) Cholinergic neural regulation of regional cerebral blood flow

    Alzheimer Disease and Associated Disorders 9:28–38.

    https://doi.org/10.1097/00002093-199505000-00007

    • PubMed
    • Google Scholar
62. 1. Schölvinck ML
    2. Maier A
    3. Ye FQ
    4. Duyn JH
    5. Leopold DA

    (2010) Neural basis of global resting-state fMRI activity

    PNAS 107:10238–10243.

    https://doi.org/10.1073/pnas.0913110107

    • PubMed
    • Google Scholar
63. 1. Shi Z
    2. Wu R
    3. Yang PF
    4. Wang F
    5. Wu TL
    6. Mishra A
    7. Chen LM
    8. Gore JC

    (2017) High spatial correspondence at a columnar level between activation and resting state fMRI signals and local field potentials

    PNAS 114:5253–5258.

    https://doi.org/10.1073/pnas.1620520114

    • PubMed
    • Google Scholar
64. 1. Shmuel A
    2. Leopold DA

    (2008) Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest

    Human Brain Mapping 29:751–761.

    https://doi.org/10.1002/hbm.20580

    • PubMed
    • Google Scholar
65. 1. Smith SM
    2. Fox PT
    3. Miller KL
    4. Glahn DC
    5. Fox PM
    6. Mackay CE
    7. Filippini N
    8. Watkins KE
    9. Toro R
    10. Laird AR
    11. Beckmann CF

    (2009) Correspondence of the brain’s functional architecture during activation and rest

    PNAS 106:13040–13045.

    https://doi.org/10.1073/pnas.0905267106

    • PubMed
    • Google Scholar
66. 1. Takata N
    2. Sugiura Y
    3. Yoshida K
    4. Koizumi M
    5. Hiroshi N
    6. Honda K
    7. Yano R
    8. Komaki Y
    9. Matsui K
    10. Suematsu M
    11. Mimura M
    12. Okano H
    13. Tanaka KF

    (2018) Optogenetic astrocyte activation evokes BOLD fMRI response with oxygen consumption without neuronal activity modulation

    Glia 66:2013–2023.

    https://doi.org/10.1002/glia.23454

    • PubMed
    • Google Scholar
67. 1. Thompson GJ
    2. Pan W-J
    3. Magnuson ME
    4. Jaeger D
    5. Keilholz SD

    (2014) Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity

    NeuroImage 84:1018–1031.

    https://doi.org/10.1016/j.neuroimage.2013.09.029

    • PubMed
    • Google Scholar
68. 1. Tong C
    2. Dai JK
    3. Chen Y
    4. Zhang K
    5. Feng Y
    6. Liang Z

    (2019) Differential coupling between subcortical calcium and BOLD signals during evoked and resting state through simultaneous calcium fiber photometry and fMRI

    NeuroImage 200:405–413.

    https://doi.org/10.1016/j.neuroimage.2019.07.006

    • PubMed
    • Google Scholar
69. 1. Tu W
    2. Zhang N

    (2022) Neural underpinning of a respiration-associated resting-state fMRI network

    eLife 11:e81555.

    https://doi.org/10.7554/eLife.81555

    • PubMed
    • Google Scholar
70. 1. Uhlirova H
    2. Kılıç K
    3. Tian P
    4. Thunemann M
    5. Desjardins M
    6. Saisan PA
    7. Sakadžić S
    8. Ness TV
    9. Mateo C
    10. Cheng Q
    11. Weldy KL
    12. Razoux F
    13. Vandenberghe M
    14. Cremonesi JA
    15. Ferri CG
    16. Nizar K
    17. Sridhar VB
    18. Steed TC
    19. Abashin M
    20. Fainman Y
    21. Masliah E
    22. Djurovic S
    23. Andreassen OA
    24. Silva GA
    25. Boas DA
    26. Kleinfeld D
    27. Buxton RB
    28. Einevoll GT
    29. Dale AM
    30. Devor A

    (2016) Cell type specificity of neurovascular coupling in cerebral cortex

    eLife 5:e14315.

    https://doi.org/10.7554/eLife.14315

    • PubMed
    • Google Scholar
71. 1. Vafaii H
    2. Mandino F
    3. Desrosiers-Grégoire G
    4. O’Connor D
    5. Markicevic M
    6. Shen X
    7. Ge X
    8. Herman P
    9. Hyder F
    10. Papademetris X
    11. Chakravarty M
    12. Crair MC
    13. Constable RT
    14. Lake EMR
    15. Pessoa L

    (2024) Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization

    Nature Communications 15:229.

    https://doi.org/10.1038/s41467-023-44363-z

    • PubMed
    • Google Scholar
72. 1. Winder AT
    2. Echagarruga C
    3. Zhang Q
    4. Drew PJ

    (2017) Weak correlations between hemodynamic signals and ongoing neural activity during the resting state

    Nature Neuroscience 20:1761–1769.

    https://doi.org/10.1038/s41593-017-0007-y

    • PubMed
    • Google Scholar
73. 1. Zhang Q
    2. Roche M
    3. Gheres KW
    4. Chaigneau E
    5. Kedarasetti RT
    6. Haselden WD
    7. Charpak S
    8. Drew PJ

    (2019) Cerebral oxygenation during locomotion is modulated by respiration

    Nature Communications 10:5515.

    https://doi.org/10.1038/s41467-019-13523-5

    • PubMed
    • Google Scholar
74. 1. Zhang X
    2. Pan WJ
    3. Keilholz SD

    (2020) The relationship between BOLD and neural activity arises from temporally sparse events

    NeuroImage 207:116390.

    https://doi.org/10.1016/j.neuroimage.2019.116390

    • PubMed
    • Google Scholar

Author details

1. Wenyu Tu

   The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3480-2098

2. Samuel R Cramer

   The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, United States

   Contribution

   Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. Nanyin Zhang

   1. The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, United States
   2. Center for Neural Engineering, Pennsylvania State University, University Park, United States
   3. Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, United States
   4. Department of Biomedical Engineering, Pennsylvania State University, University Park, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   nuz2@psu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5824-9058

• 747

  views

• 73

  downloads

• 2

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.