Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain

Carmichael, David W.; Vulliémoz, Serge; Murta, Teresa; Chaudhary, Umair J.; Perani, Suejen; Rodionov, Roman; Rosa, Maria J.; Friston, Karl J.; Lemieux, Louis

doi:10.1101/237198

Cited by 3 publications

(5 citation statements)

References 39 publications

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“…This finding was further corroborated by studying correlations between methods. In line with results obtained in simultaneous fMRI-ECoG recordings in animals (Logothetis et al, 2001; Niessing et al, 2005; Magri et al, 2012) as well as in human (Carmichael et al, 2017), we observed positive correlations between fMRI and high-frequency ECoG activity and negative correlations with low-frequency ECoG. We also observed negative correlations between EEG α band-power and BOLD in parietal, occipital and rolandic areas in line with previous reports on simultaneous recordings (Laufs et al, 2003; Moosmann et al, 2003; Ritter et al, 2009; Scheeringa et al, 2009).…”

Section: Discussionsupporting

confidence: 92%

“…Numerous studies in humans have used simultaneous fMRI–EEG recordings, including Laufs et al (2003); Moosmann et al (2003); Ritter and Villringer (2006); Scheeringa et al (2008); Ritter et al (2009); Scheeringa et al (2011, 2016). Simultaneous fMRI–ECoG recordings are only available in animals (Logothetis et al, 2001; Niessing et al, 2005; Magri et al, 2012) with a recent exception in human (Carmichael et al, 2017). Most human studies have relied instead on a common task (Mukamel et al, 2005; Nir et al, 2007; Hermes et al, 2012; Winawer et al, 2013; Harvey et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus

Haufe

DeGuzman²,

Henin

et al. 2018

NeuroImage

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Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience. Here, we address the case of complex naturalistic stimuli and ask two basic questions. First, how reliable are the responses evoked by a naturalistic audio-visual stimulus in each of these imaging methods, and second, how similar are stimulus-related responses across methods? To this end, we investigated a wide range of brain regions and frequency bands. We presented the same movie clip twice to three different cohorts of subjects (N = 45, N = 11, N = 5) and assessed stimulus-driven correlations across viewings and between imaging methods, thereby ruling out task-irrelevant confounds. All three imaging methods had similar repeat-reliability across viewings when fMRI and EEG data were averaged across subjects, highlighting the potential to achieve large signal-to-noise ratio by leveraging large sample sizes. The fMRI signal correlated positively with high-frequency ECoG power across multiple task-related cortical structures but positively with low-frequency EEG and ECoG power. In contrast to previous studies, these correlations were as strong for low-frequency as for high frequency ECoG. We also observed links between fMRI and infra-slow EEG voltage fluctuations. These results extend previous findings to the case of natural stimulus processing.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus

Haufe

DeGuzman²,

Henin

et al. 2018

NeuroImage

View full text Add to dashboard Cite

show abstract

“…This finding was further corroborated by studying correlations between methods. In line with results obtained in simultaneous fMRI-ECoG recordings in animals (Logothetis et al, 2001;Magri et al, 2012;Niessing et al, 2005) as well as in human (Carmichael et al, 2017), we observed positive correlations between fMRI and highfrequency ECoG activity and negative correlations with lowfrequency ECoG. We also observed negative correlations between EEG α band-power and BOLD in parietal, occipital and rolandic areas in line with previous reports on simultaneous recordings (Laufs et al, 2003;Moosmann et al, 2003;Ritter et al, 2009;Scheeringa et al, 2009).…”

Section: Discussionsupporting

confidence: 92%

“…Simultaneous fMRI-EEG recordings in human have been performed in Laufs et al (2003); Moosmann et al (2003); Ritter et al (2009); Ritter and Villringer (2006); Scheeringa et al (2008Scheeringa et al ( , 2011Scheeringa et al ( , 2016. Simultaneous fMRI-ECoG recordings are only available in animals (Logothetis et al, 2001;Magri et al, 2012;Niessing et al, 2005) with a recent exception in human (Carmichael et al, 2017). Most human studies have relied instead on a common task (Harvey et al, 2013;Hermes et al, 2012;Mukamel et al, 2005;Nir et al, 2007;Winawer et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Reliability and correlation of fMRI, ECoG and EEG during natural stimulus processing

Haufe

DeGuzman²,

Henin

et al. 2017

Preprint

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Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience. Here, we address the case of complex naturalistic stimuli and ask two basic questions. First, how reliable are the responses evoked by a naturalistic audio-visual stimulus in each of these imaging methods, and second, how similar are stimulus-related responses across methods? To this end, we investigated a wide range of brain regions and frequency bands. We presented the same movie clip twice to three different cohorts of subjects (N EEG = 45, N fMRI = 11, N ECoG = 5) and assessed stimulus-driven correlations across viewings and between imaging methods, thereby ruling out task-irrelevant confounds. All three imaging methods had similar repeat-reliability across viewings when fMRI and EEG data were averaged across subjects, highlighting the potential to achieve large signal-to-noise ratio by leveraging large sample sizes. The fMRI signal correlated positively with high-frequency ECoG power across multiple task-related cortical structures but positively with low-frequency EEG and ECoG power. In contrast to previous studies, these correlations were as strong for low-frequency as for high frequency ECoG. We also observed links between fMRI and infra-slow EEG voltage fluctuations. These results extend previous findings to the case of natural stimulus processing.

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“…For example, the activity level of neuronal activity declines after seizures (known as post ictal depression), which is related to the pathological evolution of ion currents ( Panayiotopoulos, 2010 ). Another motivation includes the relationship between energy metabolism due to neuronal activity (caused by ion dynamics) and their level of demand for energy as observed in haemodynamic responses ( Rosa et al., 2011 ; Carmichael et al., 2017 ).…”

Section: Validation Analyses Using Simulated Datamentioning

confidence: 99%

Adiabatic dynamic causal modelling

et al. 2021

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This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity.

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Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain

Cited by 3 publications

References 39 publications

Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus

Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus

Reliability and correlation of fMRI, ECoG and EEG during natural stimulus processing

Adiabatic dynamic causal modelling

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