Author response: Neural underpinning of a respiration-associated resting-state fMRI network

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by "electrophysiology-invisible" signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Author response: Mechanisms and functions of respiration-driven gamma oscillations in the primary olfactory cortex

Decision letter: Mechanisms and functions of respiration-driven gamma oscillations in the primary olfactory cortex

Editor's evaluation: Mechanisms and functions of respiration-driven gamma oscillations in the primary olfactory cortex

Decision letter: Prefrontal-amygdalar oscillations related to social behavior in mice

Editor's evaluation: Prefrontal-amygdalar oscillations related to social behavior in mice

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by "electrophysiology-invisible" signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by ‘electrophysiology-invisible’ signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by 'electrophysiology-invisible' signals. These findings offer a novel perspective on our understanding of RSN interpretation.

The neocortex of mammalian brains shows prominent oscillatory activity in the local field potential (LFP) and EEG signal within a broad range of frequencies from slow (1-8 Hz) delta/theta band to gamma band (40-100 Hz) and higher frequencies [1]. Oscillations in different frequency bands seem to be linked. There is increasing evidence that amplitude modulations in the gamma frequency band are phase-locked to the delta/theta rhythm [2]. Several studies have linked theta-gamma phase-amplitude coupling to cognitive processes [3]. Here we report that in awake mice gamma band LFP power in the barrel cortex is phase-locked to the concurrent theta band LFP oscillation and that this theta band oscillation is strongly correlated to the respiratory rhythm. When the animals were briefly exposed to hypoxic air, the resulting frequency increases in the respiratory rhythm were paralleled in the delta/theta band LFP oscillations (Fig.1A-D). LFP oscillations in sub-bands of the broad (40 – 100 Hz) gamma frequency band were amplitude-modulated in phase with the breathing frequency (Fig. ​(Fig.1E).1E). After removal of the olfactory bulb the frequency profile of the phase-amplitude coupling was significantly changed. Particularly the respiration-locked amplitude modulation in the high gamma band (64-128 Hz), which was prominent in healthy control mice, shifted to lower frequencies in bulbectomized mice. Our findings imply that in mice respiratory activity directly modulates delta/theta band LFP oscillations through respiration-locked olfactory bulb activity and indirectly, through phase-amplitude coupling, gamma band power. Figure 1 A. Respiration (top) and LFP (bottom two) traces during normal breathing. The respiration signal was recorded with a thermistor and its unit is arbitrary. LFP recording sites were 610 um apart. Solid curves are the signals after band-pass filtering in ...

Respiration can induce motion and CO2 fluctuation during resting-state fMRI (rsfMRI) scans, which will lead to non-neural artifacts in the rsfMRI signal. In the meantime, as a crucial physiologic process, respiration can directly drive neural activity change in the brain, and may thereby modulate the rsfMRI signal. Nonetheless, this potential neural component in the respiration-fMRI relationship is largely unexplored. To elucidate this issue, here we simultaneously recorded the electrophysiology, rsfMRI, and respiration signals in rats. Our data show that respiration is indeed associated with neural activity changes, evidenced by a phase-locking relationship between slow respiration variations and the gamma-band power of the electrophysiological signal recorded in the anterior cingulate cortex. Intriguingly, slow respiration variations are also linked to a characteristic rsfMRI network, which is mediated by gamma-band neural activity. In addition, this respiration-related brain network disappears when brain-wide neural activity is silenced at an isoelectrical state, while the respiration is maintained, further confirming the necessary role of neural activity in this network. Taken together, this study identifies a respiration-related brain network underpinned by neural activity, which represents a novel component in the respiration-rsfMRI relationship that is distinct from respiration-related rsfMRI artifacts. It opens a new avenue for investigating the interactions between respiration, neural activity, and resting-state brain networks in both healthy and diseased conditions.

The hypothesis that specific frequency components of the spontaneous local field potentials (LFPs) underlie low frequency fluctuations of resting state fMRI (rsfMRI) signals was tested. The previous analyses of rsfMRI signals revealed differential inter-regional correlations among areas 3a, 3b, and 1 of primary somatosensory cortex (S1) in anesthetized monkeys (Wang et al. [2013]: Neuron 78:1116-1126). Here LFP band(s) which correlated between S1 regions, and how these inter-regional correlation differences covaried with rsfMRI signals were examined. LFP signals were filtered into seven bands (delta, theta, alpha, beta, gamma low, gamma high, and gamma very high), and then a Hilbert transformation was applied to obtain measures of instantaneous amplitudes and temporal lags between regions of interest (ROI) digit-digit pairs (areas 3b-area 1, area 3a-area 1, area 3a-area 3b) and digit-face pairs (area 3b-face, area 1-face, and area 3a-face). It was found that variations in the inter-regional correlation strengths between digit-digit and digit-face pairs in the delta (1-4 Hz), alpha (9-14 Hz), beta (15-30 Hz), and gamma (31-50 Hz) bands parallel those of rsfMRI signals to varying degrees. Temporal lags between digit-digit area pairs varied across LFP bands, with area 3a mostly leading areas 1/2 and 3b. In summary, the data demonstrates that the low and middle frequency range (1-50 Hz) of spontaneous LFP signals similarly covary with the low frequency fluctuations of rsfMRI signals within local circuits of S1, supporting a neuronal electrophysiological basis of rsfMRI signals. Inter-areal LFP temporal lag differences provided novel insights into the directionality of information flow among S1 areas at rest. Hum Brain Mapp 37:2755-2766, 2016. © 2016 Wiley Periodicals, Inc.

Frequency-Band Coupling in Surface EEG Reflects Spiking Activity in Monkey Visual Cortex

This study aims to understand how functional connectivity (FC) between areas 3b and S2 alters following input deprivation and the neuronal basis of disrupted FC of resting-state fMRI signals. We combined submillimeter fMRI with microelectrode recordings to localize the deafferented digit regions in areas 3b and S2 by mapping tactile stimulus-evoked fMRI activations before and after cervical dorsal column lesion in each male monkey. An average afferent disruption of 97% significantly reduced fMRI, local field potential (LFP), and spike responses to stimuli in both areas. Analysis of resting-state fMRI signal correlation, LFP coherence, and spike cross-correlation revealed significantly reduced functional connectivity between deafferented areas 3b and S2. The degrees of reductions in stimulus responsiveness and FC after deafferentation differed across fMRI, LFP, and spiking signals. The reduction of FC was much weaker than that of stimulus-evoked responses. Whereas the largest stimulus-evoked signal drop (∼80%) was observed in LFP signals, the greatest FC reduction was detected in the spiking activity (∼30%). fMRI signals showed mild reductions in stimulus responsiveness (∼25%) and FC (∼20%). The overall deafferentation-induced changes were quite similar in areas 3b and S2 across signals. Here we demonstrated that FC strength between areas 3b and S2 was much weakened by dorsal column lesion, and stimulus response reduction and FC disruption in fMRI covary with those of LFP and spiking signals in deafferented areas 3b and S2. These findings have important implications for fMRI studies aiming to probe FC alterations in pathological conditions involving deafferentation in humans.SIGNIFICANCE STATEMENT By directly comparing fMRI, local field potential, and spike signals in both tactile stimulation and resting states before and after severe disruption of dorsal column afferent, we demonstrated that reduction in fMRI responses to stimuli is accompanied by weakened resting-state fMRI functional connectivity (FC) in input-deprived and reorganized digit regions in area 3b of the S1 and S2. Concurrent reductions in local field potential and spike FC validated the use of resting-state fMRI signals for probing neural intrinsic FC alterations in pathological deafferented cortex, and indicated that disrupted FC between mesoscale functionally highly related regions may contribute to the behavioral impairments.

Key pointsThe local field potential (LFP), the low‐frequency part of extracellular potentials recorded in neural tissue, is often used for probing neural circuit activity. Interpreting the LFP signal is difficult, however.While the cortical LFP is thought mainly to reflect synaptic inputs onto pyramidal neurons, little is known about the role of the various subthreshold active conductances in shaping the LFP.By means of biophysical modelling we obtain a comprehensive qualitative understanding of how the LFP generated by a single pyramidal neuron depends on the type and spatial distribution of active subthreshold currents.For pyramidal neurons, the h‐type channels probably play a key role and can cause a distinct resonance in the LFP power spectrum.Our results show that the LFP signal can give information about the active properties of neurons and imply that preferred frequencies in the LFP can result from those cellular properties instead of, for example, network dynamics.The main contribution to the local field potential (LFP) is thought to stem from synaptic input to neurons and the ensuing subthreshold dendritic processing. The role of active dendritic conductances in shaping the LFP has received little attention, even though such ion channels are known to affect the subthreshold neuron dynamics. Here we used a modelling approach to investigate the effects of subthreshold dendritic conductances on the LFP. Using a biophysically detailed, experimentally constrained model of a cortical pyramidal neuron, we identified conditions under which subthreshold active conductances are a major factor in shaping the LFP. We found that, in particular, the hyperpolarization‐activated inward current, Ih, can have a sizable effect and cause a resonance in the LFP power spectral density. To get a general, qualitative understanding of how any subthreshold active dendritic conductance and its cellular distribution can affect the LFP, we next performed a systematic study with a simplified model. We found that the effect on the LFP is most pronounced when (1) the synaptic drive to the cell is asymmetrically distributed (i.e. either basal or apical), (2) the active conductances are distributed non‐uniformly with the highest channel densities near the synaptic input and (3) when the LFP is measured at the opposite pole of the cell relative to the synaptic input. In summary, we show that subthreshold active conductances can be strongly reflected in LFP signals, opening up the possibility that the LFP can be used to characterize the properties and cellular distributions of active conductances.