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• W2123170884 abstract "Brain networks are commonly defined using correlations between blood oxygen level-dependent (BOLD) signals in different brain areas. Although evidence suggests that gamma-band (30–100 Hz) neural activity contributes to local BOLD signals, the neural basis of interareal BOLD correlations is unclear. We first defined a visual network in monkeys based on converging evidence from interareal BOLD correlations during a fixation task, task-free state, and anesthesia, and then simultaneously recorded local field potentials (LFPs) from the same four network areas in the task-free state. Low-frequency oscillations (<20 Hz), and not gamma activity, predominantly contributed to interareal BOLD correlations. The low-frequency oscillations also influenced local processing by modulating gamma activity within individual areas. We suggest that such cross-frequency coupling links local BOLD signals to BOLD correlations across distributed networks. Brain networks are commonly defined using correlations between blood oxygen level-dependent (BOLD) signals in different brain areas. Although evidence suggests that gamma-band (30–100 Hz) neural activity contributes to local BOLD signals, the neural basis of interareal BOLD correlations is unclear. We first defined a visual network in monkeys based on converging evidence from interareal BOLD correlations during a fixation task, task-free state, and anesthesia, and then simultaneously recorded local field potentials (LFPs) from the same four network areas in the task-free state. Low-frequency oscillations (<20 Hz), and not gamma activity, predominantly contributed to interareal BOLD correlations. The low-frequency oscillations also influenced local processing by modulating gamma activity within individual areas. We suggest that such cross-frequency coupling links local BOLD signals to BOLD correlations across distributed networks. Simultaneous multiple-electrode recordings from a thalamo-cortical network Low-frequency neural oscillations contributed to interareal BOLD correlations Low-frequency oscillations modulated local gamma-frequency neural activity Cross-frequency coupling linked local BOLD activations to BOLD connectivity There is currently a limited understanding of the neurophysiological basis of fMRI signals, despite the prevalence of fMRI in neuroscience research. Arguably, most progress has been made toward finding local neural signatures of blood oxygen level-dependent (BOLD) activity in individual brain areas. A number of studies have demonstrated a tight coupling between BOLD responses to sensory stimuli and power in the gamma band (30–100 Hz) of local field potential (LFP) signals (Goense and Logothetis, 2008Goense J.B. Logothetis N.K. Neurophysiology of the BOLD fMRI signal in awake monkeys.Curr. Biol. 2008; 18: 631-640Abstract Full Text Full Text PDF PubMed Scopus (417) Google Scholar; Logothetis et al., 2001Logothetis N.K. Pauls J. Augath M. Trinath T. Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal.Nature. 2001; 412: 150-157Crossref PubMed Scopus (4692) Google Scholar; Mukamel et al., 2005Mukamel R. Gelbard H. Arieli A. Hasson U. Fried I. Malach R. Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex.Science. 2005; 309: 951-954Crossref PubMed Scopus (707) Google Scholar; Niessing et al., 2005Niessing J. Ebisch B. Schmidt K.E. Niessing M. Singer W. Galuske R.A. Hemodynamic signals correlate tightly with synchronized gamma oscillations.Science. 2005; 309: 948-951Crossref PubMed Scopus (624) Google Scholar; Shmuel et al., 2006Shmuel A. Augath M. Oeltermann A. Logothetis N.K. Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1.Nat. Neurosci. 2006; 9: 569-577Crossref PubMed Scopus (722) Google Scholar). A prominent role for gamma frequencies is not limited to evoked BOLD responses, but extends to BOLD activity during the resting state. This task-free state has been related to spontaneous, slow (i.e., <0.1 Hz) fluctuations in BOLD signals (Fox and Raichle, 2007Fox M.D. Raichle M.E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.Nat. Rev. Neurosci. 2007; 8: 700-711Crossref PubMed Scopus (4937) Google Scholar). Recent evidence suggests that slow changes in the power of neural gamma oscillations make a significant contribution to the spontaneous local fluctuations of resting-state BOLD signals in humans (He et al., 2008He B.J. Snyder A.Z. Zempel J.M. Smyth M.D. Raichle M.E. Electrophysiological correlates of the brain's intrinsic large-scale functional architecture.Proc. Natl. Acad. Sci. USA. 2008; 105: 16039-16044Crossref PubMed Scopus (494) Google Scholar; Nir et al., 2007Nir Y. Fisch L. Mukamel R. Gelbard-Sagiv H. Arieli A. Fried I. Malach R. Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations.Curr. Biol. 2007; 17: 1275-1285Abstract Full Text Full Text PDF PubMed Scopus (374) Google Scholar, Nir et al., 2008Nir Y. Mukamel R. Dinstein I. Privman E. Harel M. Fisch L. Gelbard-Sagiv H. Kipervasser S. Andelman F. Neufeld M.Y. et al.Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex.Nat. Neurosci. 2008; 11: 1100-1108Crossref PubMed Scopus (361) Google Scholar) and monkeys (Schölvinck et al., 2010Schölvinck M.L. Maier A. Ye F.Q. Duyn J.H. Leopold D.A. Neural basis of global resting-state fMRI activity.Proc. Natl. Acad. Sci. USA. 2010; 107: 10238-10243Crossref PubMed Scopus (659) Google Scholar). The close relationship between gamma oscillations and BOLD activity in individual brain areas supports the notion that gamma processing reflects local neural computations (Canolty and Knight, 2010Canolty R.T. Knight R.T. The functional role of cross-frequency coupling.Trends Cogn. Sci. 2010; 14: 506-515Abstract Full Text Full Text PDF PubMed Scopus (1210) Google Scholar; Siegel et al., 2012Siegel M. Donner T.H. Engel A.K. Spectral fingerprints of large-scale neuronal interactions.Nat. Rev. Neurosci. 2012; 13: 121-134PubMed Google Scholar). Functional interactions between distributed brain areas, known as functional connectivity, give rise to coherent patterns of BOLD signals within specific neural networks during the resting state as well as behavioral tasks. Covariant relations of spontaneous BOLD signals in the resting state have been reported in the awake human (Biswal et al., 1995Biswal B. Yetkin F.Z. Haughton V.M. Hyde J.S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.Magn. Reson. Med. 1995; 34: 537-541Crossref PubMed Scopus (7037) Google Scholar; Damoiseaux et al., 2006Damoiseaux J.S. Rombouts S.A. Barkhof F. Scheltens P. Stam C.J. Smith S.M. Beckmann C.F. Consistent resting-state networks across healthy subjects.Proc. Natl. Acad. Sci. USA. 2006; 103: 13848-13853Crossref PubMed Scopus (3254) Google Scholar; Dosenbach et al., 2010Dosenbach N.U. Nardos B. Cohen A.L. Fair D.A. Power J.D. Church J.A. Nelson S.M. Wig G.S. Vogel A.C. Lessov-Schlaggar C.N. et al.Prediction of individual brain maturity using fMRI.Science. 2010; 329: 1358-1361Crossref PubMed Scopus (1437) Google Scholar; Fox et al., 2005Fox M.D. Snyder A.Z. Vincent J.L. Corbetta M. Van Essen D.C. Raichle M.E. The human brain is intrinsically organized into dynamic, anticorrelated functional networks.Proc. Natl. Acad. Sci. USA. 2005; 102: 9673-9678Crossref PubMed Scopus (6169) Google Scholar; Seeley et al., 2007Seeley W.W. Menon V. Schatzberg A.F. Keller J. Glover G.H. Kenna H. Reiss A.L. Greicius M.D. Dissociable intrinsic connectivity networks for salience processing and executive control.J. Neurosci. 2007; 27: 2349-2356Crossref PubMed Scopus (4883) Google Scholar; Wang et al., 2010Wang L. Yu C. Chen H. Qin W. He Y. Fan F. Zhang Y. Wang M. Li K. Zang Y. et al.Dynamic functional reorganization of the motor execution network after stroke.Brain. 2010; 133: 1224-1238Crossref PubMed Scopus (461) Google Scholar; Yeo et al., 2011Yeo B.T. Krienen F.M. Sepulcre J. Sabuncu M.R. Lashkari D. Hollinshead M. Roffman J.L. Smoller J.W. Zöllei L. Polimeni J.R. et al.The organization of the human cerebral cortex estimated by intrinsic functional connectivity.J. Neurophysiol. 2011; 106: 1125-1165Crossref PubMed Scopus (4106) Google Scholar) and monkey (Moeller et al., 2009Moeller S. Nallasamy N. Tsao D.Y. Freiwald W.A. Functional connectivity of the macaque brain across stimulus and arousal states.J. Neurosci. 2009; 29: 5897-5909Crossref PubMed Scopus (54) Google Scholar), as well as the anesthetized monkey (Vincent et al., 2007Vincent J.L. Patel G.H. Fox M.D. Snyder A.Z. Baker J.T. Van Essen D.C. Zempel J.M. Snyder L.H. Corbetta M. Raichle M.E. Intrinsic functional architecture in the anaesthetized monkey brain.Nature. 2007; 447: 83-86Crossref PubMed Scopus (1390) Google Scholar) and rat (Lu et al., 2007Lu H. Zuo Y. Gu H. Waltz J.A. Zhan W. Scholl C.A. Rea W. Yang Y. Stein E.A. Synchronized delta oscillations correlate with the resting-state functional MRI signal.Proc. Natl. Acad. Sci. USA. 2007; 104: 18265-18269Crossref PubMed Scopus (325) Google Scholar, Lu et al., 2012Lu H. Zou Q. Gu H. Raichle M.E. Stein E.A. Yang Y. Rat brains also have a default mode network.Proc. Natl. Acad. Sci. USA. 2012; 109: 3979-3984Crossref PubMed Scopus (391) Google Scholar). Resting-state connectivity studies have proven useful for characterizing network architectures and for exploring pathological alterations in neurological and psychiatric diseases (Greicius, 2008Greicius M. Resting-state functional connectivity in neuropsychiatric disorders.Curr. Opin. Neurol. 2008; 21: 424-430Crossref PubMed Google Scholar; Matthews et al., 2006Matthews P.M. Honey G.D. Bullmore E.T. Applications of fMRI in translational medicine and clinical practice.Nat. Rev. Neurosci. 2006; 7: 732-744Crossref PubMed Scopus (245) Google Scholar; Zhang and Raichle, 2010Zhang D. Raichle M.E. Disease and the brain's dark energy.Nat. Rev. Neurol. 2010; 6: 15-28Crossref PubMed Scopus (686) Google Scholar). Although there has been a rapid increase in the number of resting-state connectivity studies and in the use of functional connectivity measures in general, there have been few studies of the neural basis of BOLD connectivity. This is at least partly due to the technical difficulty of obtaining simultaneous recordings from multiple network sites using depth electrodes in awake humans or animals. The only such study to date reported that gamma oscillations most strongly correlated with BOLD connectivity between auditory cortices in epilepsy patients (Nir et al., 2008Nir Y. Mukamel R. Dinstein I. Privman E. Harel M. Fisch L. Gelbard-Sagiv H. Kipervasser S. Andelman F. Neufeld M.Y. et al.Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex.Nat. Neurosci. 2008; 11: 1100-1108Crossref PubMed Scopus (361) Google Scholar), similar to the relationship previously reported between gamma oscillations and local BOLD signals. However, it is not clear whether the link between gamma oscillations and BOLD connectivity generalizes to other circuits. It is important to test how sensitive BOLD connectivity is to oscillatory frequencies lower than gamma because it is not necessary for local computation and large-scale communication to recruit the same frequencies of oscillatory activity. Rather, low frequencies may be advantageous and commonly used for interactions between distant brain areas (Fujisawa and Buzsáki, 2011Fujisawa S. Buzsáki G. A 4 Hz oscillation adaptively synchronizes prefrontal, VTA, and hippocampal activities.Neuron. 2011; 72: 153-165Abstract Full Text Full Text PDF PubMed Scopus (321) Google Scholar; Siegel et al., 2012Siegel M. Donner T.H. Engel A.K. Spectral fingerprints of large-scale neuronal interactions.Nat. Rev. Neurosci. 2012; 13: 121-134PubMed Google Scholar). A number of electrophysiological studies have demonstrated that brain oscillations show statistically nested coupling, with low frequencies modulating high frequencies (Buzsáki and Wang, 2012Buzsáki G. Wang X.J. Mechanisms of gamma oscillations.Annu. Rev. Neurosci. 2012; 35: 203-225Crossref PubMed Scopus (1484) Google Scholar; Jensen and Colgin, 2007Jensen O. Colgin L.L. Cross-frequency coupling between neuronal oscillations.Trends Cogn. Sci. 2007; 11: 267-269Abstract Full Text Full Text PDF PubMed Scopus (636) Google Scholar; Schroeder and Lakatos, 2009Schroeder C.E. Lakatos P. Low-frequency neuronal oscillations as instruments of sensory selection.Trends Neurosci. 2009; 32: 9-18Abstract Full Text Full Text PDF PubMed Scopus (1002) Google Scholar). Given that different oscillations are associated with different spatiotemporal scales (Buzsáki and Draguhn, 2004Buzsáki G. Draguhn A. Neuronal oscillations in cortical networks.Science. 2004; 304: 1926-1929Crossref PubMed Scopus (4105) Google Scholar; von Stein and Sarnthein, 2000von Stein A. Sarnthein J. Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization.Int. J. Psychophysiol. 2000; 38: 301-313Crossref PubMed Scopus (1124) Google Scholar), cross-frequency coupling may integrate information transmission over a large-scale network with local cortical processing (Canolty and Knight, 2010Canolty R.T. Knight R.T. The functional role of cross-frequency coupling.Trends Cogn. Sci. 2010; 14: 506-515Abstract Full Text Full Text PDF PubMed Scopus (1210) Google Scholar). We thus hypothesized that (1) BOLD functional connectivity predominantly reflects low-frequency neural interactions between remote brain areas (e.g., alpha [8–13 Hz] and theta [4–8 Hz]); (2) low frequencies modulate local high-frequency activity (e.g., gamma), which predominantly reflects BOLD signals from an individual area; and (3) such cross-frequency coupling links BOLD correlations in distributed network nodes to local BOLD activations. To test our hypotheses, we first mapped out thalamo-cortical networks (i.e., network defined as a set of interconnected brain regions) derived from BOLD signals acquired from macaque monkeys. Given that task-free fMRI studies have involved various experimental conditions in humans (free gaze, eyes closed, and fixation) and monkeys (free gaze and anesthesia), our study incorporated three experimental conditions to allow generalization and ready comparison with the literature: a task-free, free-gaze condition, defined as resting state here; a fixation task; and anesthesia. We focused on a thalamo-cortical visual network constituted by the lateral intraparietal area (LIP), the temporal occipital area (TEO), area V4, and the pulvinar, which has been well studied in terms of its anatomical connectivity (e.g., Felleman and Van Essen, 1991Felleman D.J. Van Essen D.C. Distributed hierarchical processing in the primate cerebral cortex.Cereb. Cortex. 1991; 1: 1-47Crossref PubMed Scopus (5444) Google Scholar; Saalmann et al., 2012Saalmann Y.B. Pinsk M.A. Wang L. Li X. Kastner S. The pulvinar regulates information transmission between cortical areas based on attention demands.Science. 2012; 337: 753-756Crossref PubMed Scopus (593) Google Scholar; Shipp, 2003Shipp S. The functional logic of cortico-pulvinar connections.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2003; 358: 1605-1624Crossref PubMed Scopus (263) Google Scholar; Ungerleider et al., 2008Ungerleider L.G. Galkin T.W. Desimone R. Gattass R. Cortical connections of area V4 in the macaque.Cereb. Cortex. 2008; 18: 477-499Crossref PubMed Scopus (218) Google Scholar). After verifying BOLD correlations across our visual network, we performed simultaneous electrophysiological recordings from the same four network areas and measured their functional connectivity based on LFPs. We included a thalamic nucleus, the pulvinar, in our study because the limited evidence available suggests that the thalamus makes an important contribution to cortical oscillations (Hughes et al., 2004Hughes S.W. Lörincz M. Cope D.W. Blethyn K.L. Kékesi K.A. Parri H.R. Juhász G. Crunelli V. Synchronized oscillations at alpha and theta frequencies in the lateral geniculate nucleus.Neuron. 2004; 42: 253-268Abstract Full Text Full Text PDF PubMed Scopus (210) Google Scholar; Saalmann et al., 2012Saalmann Y.B. Pinsk M.A. Wang L. Li X. Kastner S. The pulvinar regulates information transmission between cortical areas based on attention demands.Science. 2012; 337: 753-756Crossref PubMed Scopus (593) Google Scholar; Steriade and Llinás, 1988Steriade M. Llinás R.R. The functional states of the thalamus and the associated neuronal interplay.Physiol. Rev. 1988; 68: 649-742PubMed Google Scholar). We used a combination of fMRI retinotopic mapping (Arcaro et al., 2011Arcaro M.J. Pinsk M.A. Li X. Kastner S. Visuotopic organization of macaque posterior parietal cortex: a functional magnetic resonance imaging study.J. Neurosci. 2011; 31: 2064-2078Crossref PubMed Scopus (83) Google Scholar) and high-resolution structural MRI scans to target the four interconnected visual areas (Shipp, 2003Shipp S. The functional logic of cortico-pulvinar connections.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2003; 358: 1605-1624Crossref PubMed Scopus (263) Google Scholar; Ungerleider et al., 2008Ungerleider L.G. Galkin T.W. Desimone R. Gattass R. Cortical connections of area V4 in the macaque.Cereb. Cortex. 2008; 18: 477-499Crossref PubMed Scopus (218) Google Scholar). Because inaccurate regions of interest (ROIs) have a detrimental effect on connectivity estimates (Smith et al., 2011Smith S.M. Miller K.L. Salimi-Khorshidi G. Webster M. Beckmann C.F. Nichols T.E. Ramsey J.D. Woolrich M.W. Network modelling methods for FMRI.Neuroimage. 2011; 54: 875-891Crossref PubMed Scopus (1276) Google Scholar), the retinotopic mapping ensured that the spatial ROIs we used to extract average time series matched functional areal boundaries. The brain activation pattern evoked by the retinotopic mapping task was projected to the corresponding structural surface (see Figures S1A and S1B available online) to accurately delineate the border of cortical regions LIP, TEO, and V4 (Figure 1). The subcortical region, the pulvinar, was manually delineated based on anatomical criteria using high-resolution structural images (Figure 1). We first aimed to show fMRI networks consistent with previous macaque studies (Moeller et al., 2009Moeller S. Nallasamy N. Tsao D.Y. Freiwald W.A. Functional connectivity of the macaque brain across stimulus and arousal states.J. Neurosci. 2009; 29: 5897-5909Crossref PubMed Scopus (54) Google Scholar; Vincent et al., 2007Vincent J.L. Patel G.H. Fox M.D. Snyder A.Z. Baker J.T. Van Essen D.C. Zempel J.M. Snyder L.H. Corbetta M. Raichle M.E. Intrinsic functional architecture in the anaesthetized monkey brain.Nature. 2007; 447: 83-86Crossref PubMed Scopus (1390) Google Scholar), by calculating intrinsic voxelwise functional connectivity during anesthesia, the resting state, and a fixation task. For the anesthesia condition, we used the right LIP as the seed region to allow direct comparison with previous work (Moeller et al., 2009Moeller S. Nallasamy N. Tsao D.Y. Freiwald W.A. Functional connectivity of the macaque brain across stimulus and arousal states.J. Neurosci. 2009; 29: 5897-5909Crossref PubMed Scopus (54) Google Scholar; Vincent et al., 2007Vincent J.L. Patel G.H. Fox M.D. Snyder A.Z. Baker J.T. Van Essen D.C. Zempel J.M. Snyder L.H. Corbetta M. Raichle M.E. Intrinsic functional architecture in the anaesthetized monkey brain.Nature. 2007; 447: 83-86Crossref PubMed Scopus (1390) Google Scholar). We calculated the correlation between the average time series from the right LIP and the time series from all other brain voxels, with the confounding variables regressed out. The right LIP showed significant connectivity (p < 0.001, corrected using Monte Carlo simulation) with the left LIP and the frontal eye field bilaterally (Figure S1C), as previously shown (Moeller et al., 2009Moeller S. Nallasamy N. Tsao D.Y. Freiwald W.A. Functional connectivity of the macaque brain across stimulus and arousal states.J. Neurosci. 2009; 29: 5897-5909Crossref PubMed Scopus (54) Google Scholar; Vincent et al., 2007Vincent J.L. Patel G.H. Fox M.D. Snyder A.Z. Baker J.T. Van Essen D.C. Zempel J.M. Snyder L.H. Corbetta M. Raichle M.E. Intrinsic functional architecture in the anaesthetized monkey brain.Nature. 2007; 447: 83-86Crossref PubMed Scopus (1390) Google Scholar). This connectivity pattern was consistent across all six monkeys. To establish functional connectivity across the visual thalamo-cortical network in the resting state, we performed a correlation analysis for our four ROIs, seeding LIP, V4, TEO, and the pulvinar in turn, during the awake conditions. There was robust connectivity between each seed region and the other ROIs. Figure 1 shows that the right V4 seed significantly correlated (p < 0.001, corrected using Monte Carlo simulation) with the ipsilateral LIP, TEO and the pulvinar (the same was true for the left V4 seed). Because the resting-state and fixation conditions showed a consistent functional connectivity pattern (Figure S1D), we combined the two conditions to increase the statistical power of the ROI-based analyses. These findings suggest that the architecture of spontaneous functional connectivity is robust across different resting-state conditions and can be replicated across animals. To allow subsequent comparison with the electrophysiological results, we next evaluated ROI-based BOLD functional connectivity between LIP, TEO, V4, and the pulvinar in the right hemisphere for the resting state and fixation task. The average time series from each ROI was extracted for each run in the native space, and Pearson's correlation coefficients between those time series were calculated for the epochs (437 ± 241 s) that were not contaminated by head movement. There was a significant correlation between each pair of regions (one-sample t test, p < 0.001; Figure S2). To control for the effect of eye movements, we also calculated Pearson's correlation between the BOLD activities corresponding to each stable-eye epoch (≥6.4 s) and observed a significant correlation between the ROIs (p < 0.01; Figure 2). Having established a robust resting-state fMRI network between V4, TEO, LIP, and the pulvinar, we next probed the electrophysiological basis of this BOLD connectivity. We derived power time series from the magnitude of the Hilbert transform for different frequency bands (Figure 3) from the LFPs simultaneously recorded in the pulvinar, LIP, TEO, and V4 (58 sessions from two monkeys, one of which was also scanned under anesthesia; see Figure S3 for finer frequency band divisions). These power time series were then band-pass filtered to 0.01–0.1 Hz to correspond to the main frequencies constituting the BOLD signal (Fox and Raichle, 2007Fox M.D. Raichle M.E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.Nat. Rev. Neurosci. 2007; 8: 700-711Crossref PubMed Scopus (4937) Google Scholar). We performed correlation analyses on long and short epochs of the power time series. The long epochs included eye movements, as commonly used in resting-state studies, thereby allowing comparison with published results, whereas the short epochs only included stable eye positions (no eye movements; see Supplemental Experimental Procedures for eye movement controls). The correlation analyses on long epochs (184 ± 84 s) showed significant correlations of power time series between ROIs for all frequency bands (one-sample t tests, p < 0.001). However, the low-frequency bands (theta, alpha, and beta) showed significantly higher correlation values than the gamma band (paired-sample t tests, p < 0.001, theta/alpha/beta versus gamma). Among the low-frequency bands, there were moderately but significantly higher correlation values for the alpha band compared with the theta and beta bands (p < 0.001, alpha versus theta/beta; p > 0.05, theta versus beta). Similarly, for stable-eye epochs, significant correlations were found in the power time series derived from all frequency bands (one-sample t tests, p < 0.001; Figures 3 and S3); but the low-frequency bands had significantly higher correlation values than the gamma band (paired-sample t tests, p < 0.001, theta/alpha/beta versus gamma), with the alpha band being moderately but significantly higher than the theta and beta bands (p < 0.001, alpha versus theta/beta; p > 0.05, theta versus beta). Overall, these results indicate that slow fluctuations in the power of low-frequency oscillations contributed most to the connectivity. To verify that power correlations predominantly resulted from slow oscillations (<0.1 Hz), we also applied the correlation analyses to the signals derived from band-pass filtering the power time series in two higher-frequency bands (0.1–1 Hz and >1 Hz). There were significantly higher correlation values for the 0.01–0.1 Hz band compared with both the 0.1–1 Hz band and the >1 Hz band (paired-sample t tests, p < 0.001). The major contribution of frequencies < 0.1 Hz is consistent with a recent study of bilateral primary auditory cortex (Nir et al., 2008Nir Y. Mukamel R. Dinstein I. Privman E. Harel M. Fisch L. Gelbard-Sagiv H. Kipervasser S. Andelman F. Neufeld M.Y. et al.Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex.Nat. Neurosci. 2008; 11: 1100-1108Crossref PubMed Scopus (361) Google Scholar). However, our findings suggest that in contrast to the current view on the predominant contribution from gamma activity, low-frequency oscillations are a major contributor to large-scale network connectivity. Slow oscillations (<0.1 Hz) are commonly thought to signal general changes in network excitability (Hughes et al., 2011Hughes S.W. Lorincz M.L. Parri H.R. Crunelli V. Infraslow (<0.1 Hz) oscillations in thalamic relay nuclei basic mechanisms and significance to health and disease states.Prog. Brain Res. 2011; 193: 145-162Crossref PubMed Scopus (78) Google Scholar; Monto et al., 2008Monto S. Palva S. Voipio J. Palva J.M. Very slow EEG fluctuations predict the dynamics of stimulus detection and oscillation amplitudes in humans.J. Neurosci. 2008; 28: 8268-8272Crossref PubMed Scopus (320) Google Scholar), whereas oscillations on a faster timescale (>1 Hz) may be better suited to more specific information exchange between areas. To measure interactions between network areas on a faster timescale, we calculated the coherence between the “raw” LFP signals (cf. power time series in the previous section) in each pair of network areas. The coherence measures the linear association between the LFPs as a function of oscillation frequency. For each recording session, we used multitaper methods (three tapers and ±4 Hz bandwidth) to estimate the coherence in every 500 ms time window for which there was no eye movement (excluding 0–200 ms after any preceding eye movement). The population mean coherence spectrum for each ROI pair showed the peak coherence at low frequencies (<20 Hz; Figure 4). Within a specified frequency band, we counted the number of sessions showing significant coherence for each pair of ROIs (jackknife variance estimates, p < 0.001). There was significant coherence in the 4–20 Hz range for 41–55 sessions (range across the six pairs of ROIs) out of the total of 58 sessions, whereas only 9–29 out of 58 sessions showed significant coherence in the 30–100 Hz range. Notably, the rank of connection strengths based on mean alpha coherence was similar to that seen in BOLD connectivity (Figure 2). For example, alpha coherence and BOLD connectivity both showed the strongest connection between the pulvinar and V4 and the weakest connection between the TEO and LIP. With respect to the greater effects at low versus high frequencies, these coherence results were consistent with that observed in the slow-wave power correlations. Thus, the coherence of neural activities on a fast timescale may give rise to the power correlation of band-limited neural activities at the slow fMRI timescale. Specifically, low-frequency oscillations (<20 Hz) may predominantly contribute to resting-state functional connectivity. Different frequencies of neural oscillations may be useful for different temporal and spatial scales: high frequencies like gamma for local computation, and lower frequencies like alpha for large-scale interactions. Because low-frequency oscillations have been shown to modulate high-frequency activity (Buzsáki and Wang, 2012Buzsáki G. Wang X.J. Mechanisms of gamma oscillations.Annu. Rev. Neurosci. 2012; 35: 203-225Crossref PubMed Scopus (1484) Google Scholar; Canolty and Knight, 2010Canolty R.T. Knight R.T. The functional role of cross-frequency coupling.Trends Cogn. Sci. 2010; 14: 506-515Abstract Full Text Full Text PDF PubMed Scopus (1210) Google Scholar; Jensen and Colgin, 2007Jensen O. Colgin L.L. Cross-frequency coupling between neuronal oscillations.Trends Cogn. Sci. 2007; 11: 267-269Abstract Full Text Full Text PDF PubMed Scopus (636) Google Scholar; Schroeder and Lakatos, 2009Schroeder C.E. Lakatos P. Low-frequency neuronal oscillations as instruments of sensory selection.Trends Neurosci. 2009; 32: 9-18Abstract Full Text Full Text PDF PubMed Scopus (1002) Google Scholar), such cross-frequency coupling may integrate functions across multiple spatiotemporal scales. We hypothesized that low frequencies may contribute to fluctuation of the power in the gamma band through a cross-frequency coupling mechanism. To measure cross-frequency coupling, we used the synchronization index (SI; Cohen, 2008Cohen M.X. Assessing transient cross-frequency coupling in EEG data.J. Neurosci. Methods. 2008; 168: 494-499Crossref PubMed Scopus (183) Google Scholar), which su" @default.
• W2123170884 created "2016-06-24" @default.
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• W2123170884 date "2012-12-01" @default.
• W2123170884 modified "2023-10-11" @default.
• W2123170884 title "Electrophysiological Low-Frequency Coherence and Cross-Frequency Coupling Contribute to BOLD Connectivity" @default.
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