Occurrence of infraslow potential oscillations in relation to task, ability to concentrate and intelligence

Trimmel, Michael; Mikowitsch, Adelheid; Groll-Knapp, E.; Haider, M.

doi:10.1016/0167-8760(90)90070-t

Cited by 13 publications

(8 citation statements)

References 4 publications

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“…These slow neural changes are potentially compatible in time scale with the slow BOLD fluctuations that occur in spontaneous activity, and are postulated to coordinate activity between large-scale brain regions (Monto et al, 2008; Steriade, 2001), making them excellent candidates for the neural basis of BOLD functional connectivity (Drew et al, 2008). The neural infra-slow signal has been a controversial issue in historical electrophysiological studies, but recent studies have demonstrated that slow cortical potentials are more likely of neural origination rather than merely artificial drifts (He and Raichle, 2009; Monto et al, 2008; Rosler et al, 1997; Trimmel et al, 1990). No meaningful correlations between infraslow electrical variations and fMRI were found during simultaneous recording and imaging in a dead rat (Fig.…”

Section: Discussionmentioning

confidence: 99%

Infraslow LFP correlates to resting-state fMRI BOLD signals

et al. 2013

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The slow fluctuations of the blood-oxygenation-level dependent (BOLD) signal in resting-state fMRI are widely utilized as a surrogate marker of ongoing neural activity. Spontaneous neural activity includes a broad range of frequencies, from infraslow (< 0.5 Hz) fluctuations to fast action potentials. Recent studies have demonstrated a correlative relationship between the BOLD fluctuations and power modulations of the local field potential (LFP), particularly in the gamma band. However, the relationship between the BOLD signal and the infraslow components of the LFP, which are directly comparable in frequency to the BOLD fluctuations, has not been directly investigated. Here we report a first examination of the temporal relation between the resting-state BOLD signal and infraslow LFPs using simultaneous fMRI and full-band LFP recording in rat. The spontaneous BOLD signal at the recording sites exhibited significant localized correlation with the infraslow LFP signals as well as with the slow power modulations of higher-frequency LFPs (1-100 Hz) at a delay comparable to the hemodynamic response time under anesthesia. Infraslow electrical activity has been postulated to play a role in attentional processes, and the findings reported here suggest that infraslow LFP coordination may share a mechanism with the large-scale BOLD-based networks previously implicated in task performance, providing new insight into the mechanisms contributing to the resting state fMRI signal.

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Section: Discussionmentioning

confidence: 99%

Infraslow LFP correlates to resting-state fMRI BOLD signals

et al. 2013

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“…If, in humans, part of the default mode and task positive networks arises from the spatiotemporal structure of a QPP, then a greater QPP strength would result in more anti-correlation being measured, and thus better performance. While it is purely speculative, it is possible that some pattern of infraslow LFP fluctuations is actually a single mechanism behind these two “networks.” This may explain why some groups have seen a relationship between infraslow EEG and performance as well (Demanuele et al, 2010; Monto et al, 2008; Trimmel et al, 1990; Trimmel et al, 2001)…”

Section: Discussionmentioning

confidence: 99%

“…These networks have been linked to numerous diseases and behavioral variations: fairly early they were linked to Alzheimer's disease (Greicius et al, 2004; Rombouts et al, 2005), but much recent interest has been in their connection to the ability to maintain attention (Eichele et al, 2008; Kelly et al, 2008; Prado and Weissman, 2011; Thompson et al, 2012; Weissman et al, 2006). Interestingly, a few groups have also found a link between attention or reaction time and infraslow EEG potentials (Helps et al, 2008; Helps et al, 2010; Trimmel et al, 1990; Trimmel et al, 2001). Future work could explore this potential from either direction, to measure infraslow EEG's spatiotemporal patterns and compare it to QPP from simultaneous or separately recorded fMRI studies, or to compare QPP to behavior and see if its relationship is similar to infraslow EEG.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2014

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Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders.

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“…Although the majority of EEG-related studies do not examine signals below 1 Hz, their existence has nevertheless been documented for many years in both humans (Monto et al, 2008;Trimmel et al, 1990;Vanhatalo et al, 2005) and animals (Aladjalova, 1957;Filippov et al, 2002;Pan et al, 2013). Evidence shows that spontaneous waveforms at these frequencies are a feature of recordings made in preterm neonates (Vanhatalo et al, 2002(Vanhatalo et al, , 2005 and in sleeping adults (Vanhatalo et al, 2004(Vanhatalo et al, , 2005.…”

Section: Behavioral and Physiological Relevance Of Infraslow Activitymentioning

confidence: 99%

Infraslow Electroencephalographic and Dynamic Resting State Network Activity

et al. 2017

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A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

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Occurrence of infraslow potential oscillations in relation to task, ability to concentrate and intelligence

Cited by 13 publications

References 4 publications

Infraslow LFP correlates to resting-state fMRI BOLD signals

Infraslow LFP correlates to resting-state fMRI BOLD signals

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

Infraslow Electroencephalographic and Dynamic Resting State Network Activity

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