Phase-amplitude coupling and infraslow (&lt;1 Hz) frequencies in the rat brain: relationship to resting state fMRI

Thompson, Garth J.; Pan, Wen-Ju; Billings, Jacob; Grooms, Joshua Koehler; Shakil, Sadia; Jaeger, Dieter; Keilholz, Shella

doi:10.3389/fnint.2014.00041

Cited by 42 publications

(52 citation statements)

References 60 publications

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“…It has been hypothesized that the infraslow oscillations are driven by changes in power in the higher frequencies or themselves modulate higher-frequency power. However, an analysis of simultaneous microelectrode recording and rsMRI in the rat found that the two processes contributed separately to the BOLD signal (Thompson et al, 2014). Studies in humans are also consistent with the idea that networks may exhibit meaningful variability overlaid on a consistent pattern of coordinated activity.…”

Section: Nested Scales Of Electrical Activity and Bold Correlatesmentioning

confidence: 62%

The Neural Basis of Time-Varying Resting-State Functional Connectivity

2014

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Dynamic network analysis based on resting-state magnetic resonance imaging (rsMRI) is a fairly new and potentially powerful tool for neuroscience and clinical research. Dynamic analysis can be sensitive to changes that occur in psychiatric or neurologic disorders and can detect variations related to performance on individual trials in healthy subjects. However, the appearance of time-varying connectivity can also arise in signals that share no temporal information, complicating the interpretation of dynamic functional connectivity studies. Researchers have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals and in humans. In this article, we review findings that link changes in electrically recorded brain states to changes in the networks obtained with rsMRI and discuss some of the challenges inherent in interpretation of these studies. The literature suggests that multiple brain processes may contribute to the dynamics observed, and we speculate that it may be possible to separate particular aspects of the rsMRI signal to enhance sensitivity to certain types of neural activity, providing new tools for basic neuroscience and clinical research.

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Section: Nested Scales Of Electrical Activity and Bold Correlatesmentioning

confidence: 62%

The Neural Basis of Time-Varying Resting-State Functional Connectivity

2014

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“…The cross-frequency relationship can also be established on the phase-amplitude coupling (Canolty & Knight, 2010). For instance, the coupling between the phase of infraslow frequency neural fluctuations and the amplitude of high-frequency neural oscillations has been reported (Thompson et al, 2014;Vanhatalo et al, 2004). As a vital characteristic of neural activities, the cross-frequency relationship has received more attention (Canolty & Knight, 2010).…”

Section: The Similarity Between Ssbrs and Ssepsmentioning

confidence: 97%

“…These findings indicate that regions for higherorder cognition also possess the characteristics of SSR. Second, in the infraslow frequency range, the BOLD signal fluctuations have been associated with electrophysiological signals (Hiltunen et al, 2014;Thompson et al, 2014;Pan, Thompson, Magnuson, Jaeger, & Keilholz, 2013;Vanhatalo et al, 2004) and behavioral fluctuations (Hiltunen et al, 2014;Palva & Palva, 2012;Thut et al, 2012;Monto, Palva, Voipio, & Palva, 2008;Leopold, Murayama, & Logothetis, 2003) and have been widely studied in resting states (Zang et al, 2007;Vanhatalo et al, 2004;Biswal, Zerrin Yetkin, Haughton, & Hyde, 1995). These studies suggest the physiological significance of low-frequency BOLD fluctuations.…”

Section: Introductionmentioning

confidence: 97%

Steady-state BOLD Response to Higher-order Cognition Modulates Low-Frequency Neural Oscillations

Wang

Gang-shu

Liu

et al. 2015

Journal of Cognitive Neuroscience

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Steady-state responses (SSRs) reflect the synchronous neural oscillations evoked by noninvasive and consistently repeated stimuli at the fundamental or harmonic frequencies. The steady-state evoked potentials (SSEPs; the representative form of the SSRs) have been widely used in the cognitive and clinical neurosciences and brain-computer interface research. However, the steady-state evoked potentials have limitations in examining high-frequency neural oscillations and basic cognition. In addition, synchronous neural oscillations in the low frequency range (<1 Hz) and in higher-order cognition have received a little attention. Therefore, we examined the SSRs in the low frequency range using a new index, the steady-state BOLD responses (SSBRs) evoked by semantic stimuli. Our results revealed that the significant SSBRs were induced at the fundamental frequency of stimuli and the first harmonic in task-related regions, suggesting the enhanced variability of neural oscillations entrained by exogenous stimuli. The SSBRs were independent of neurovascular coupling and characterized by sensorimotor bias, an indication of regional-dependent neuroplasticity. Furthermore, the amplitude of SSBRs may predict behavioral performance and show the psychophysiological relevance. Our findings provide valuable insights into the understanding of the SSRs evoked by higher-order cognition and how the SSRs modulate low-frequency neural oscillations.

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“…First, high doses of isoflurane induce a burst state, which means that all neural activity alternates between a "low" state where there is little activity and a "high," activated state (see Appendix A of Thompson et al 2013c). In our previous work, we used the burst state to explain observing correlation between high and low neural frequencies as they related to rsfMRI in only a single case under isoflurane ( Thompson et al 2014). This is because during a low state most activity is suspended simultaneously and thus would correlate whether or not it would have correlated had there been no bursting.…”

Section: Summary Of Resultsmentioning

confidence: 99%

“…As a preliminary attempt to determine whether the high and low frequencies represented separable processes, we examined phase amplitude coupling between high-and lowfrequency electrical activity and compared static rsfMRI to both of these frequency bands (Thompson et al 2014). We discovered that the different frequencies of electrical activity appeared to contribute to two separate patterns of BOLD signal fluctuations that were then combined into the measured rsfMRI signal.…”

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confidence: 99%

Different dynamic resting state fMRI patterns are linked to different frequencies of neural activity

Thompson

Pan

Keilholz

2015

Journal of Neurophysiology

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Thompson GJ, Pan WJ, Keilholz SD. Different dynamic resting state fMRI patterns are linked to different frequencies of neural activity. J Neurophysiol 114: 114 -124, 2015. First published June 3, 2015 doi:10.1152/jn.00235.2015.-Resting state functional magnetic resonance imaging (rsfMRI) results have indicated that network mapping can contribute to understanding behavior and disease, but it has been difficult to translate the maps created with rsfMRI to neuroelectrical states in the brain. Recently, dynamic analyses have revealed multiple patterns in the rsfMRI signal that are strongly associated with particular bands of neural activity. To further investigate these findings, simultaneously recorded invasive electrophysiology and rsfMRI from rats were used to examine two types of electrical activity (directly measured low-frequency/infraslow activity and band-limited power of higher frequencies) and two types of dynamic rsfMRI (quasi-periodic patterns or QPP, and sliding window correlation or SWC). The relationship between neural activity and dynamic rsfMRI was tested under three anesthetic states in rats: dexmedetomidine and high and low doses of isoflurane. Under dexmedetomidine, the lightest anesthetic, infraslow electrophysiology correlated with QPP but not SWC, whereas band-limited power in higher frequencies correlated with SWC but not QPP. Results were similar under isoflurane; however, the QPP was also correlated to band-limited power, possibly due to the burst-suppression state induced by the anesthetic agent. The results provide additional support for the hypothesis that the two types of dynamic rsfMRI are linked to different frequencies of neural activity, but isoflurane anesthesia may make this relationship more complicated. Understanding which neural frequency bands appear as particular dynamic patterns in rsfMRI may ultimately help isolate components of the rsfMRI signal that are of interest to disorders such as schizophrenia and attention deficit disorder.resting state fMRI; dynamic analysis; quasi-periodic patterns; sliding window correlation; simultaneous electrophysiology and MRI

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Phase-amplitude coupling and infraslow (<1 Hz) frequencies in the rat brain: relationship to resting state fMRI

Cited by 42 publications

References 60 publications

The Neural Basis of Time-Varying Resting-State Functional Connectivity

The Neural Basis of Time-Varying Resting-State Functional Connectivity

Steady-state BOLD Response to Higher-order Cognition Modulates Low-Frequency Neural Oscillations

Different dynamic resting state fMRI patterns are linked to different frequencies of neural activity

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