Long-term reliability of the visual EEG Poffenberger paradigm

Friedrich, Patrick; Ocklenburg, Sebastian; Mochalski, Lisa N.; Güntürkün, Onur; Genç, Erhan

doi:10.1016/j.bbr.2017.05.019

Cited by 17 publications

(23 citation statements)

References 42 publications

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“…In the literature, IHTT values have also been shown to be inconsistent across participants and counterintuitively, in some cases have even been calculated as negative (e.g. Saron and Davidson, 1989;Marzi et al, 1991;Westerhausen et al, 2006;Friedrich et al, 2017), the latter suggesting that interhemispheric transfer evaluated at the electrode and at the single-subject level can produce either inaccurate results or in disagreement with our assumptions that evoked activity produces a contralateral activation prior to the interhemispheric transfer.…”

Section: Estimation Of the Ihtt And Conduction Velocitycontrasting

confidence: 68%

“…g . Chaumillon et al, 2018; Saron and Foxe, 2003; Westerhausen et al, 2006; Whitford et al, 2011; Friedrich et al, 2017). In our study, we opted for a CD-based estimation of the IHTT as the closest reflection of the evoked neural activity in the regions of interest, consistent with the corresponding white matter tract selection (section 3.2.1).…”

Section: Discussionmentioning

confidence: 99%

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In-vivo estimation of axonal morphology from MRI and EEG data

Oliveira

Pelentritou

Domenicantonio

et al. 2022

Preprint

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Purpose: We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in-vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity. Theory: The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on 1) the distribution of axonal radius - P(r) - and 2) the g-ratio of the individual axons that compose this tract - g(r). P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power-law with parameters α and β. Methods: MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract. Results: The estimated interhemispheric transfer time was 11.72±2.87 ms, leading to an average conduction velocity across subjects of 13.22±1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ - the width of the right tail of the axonal radius distribution and β - the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40±0.07 μm and the parameter β was 0.67±0.02 μm^(-α). Conclusions: The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in-vivo histological studies of the human brain.

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Section: Estimation Of the Ihtt And Conduction Velocitycontrasting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

In-vivo estimation of axonal morphology from MRI and EEG data

Oliveira

Pelentritou

Domenicantonio

et al. 2022

Preprint

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“…The only solution is then to average values obtained for each direction to cancel out the eye dominance influence meaning that, based on the behavior, only a global IHTT evaluation is possible (i.e., without considering the direction of the transfer). These results and their interpretation underlying the difficulties of using behavioral data were published separately (Chaumillon et al, 2014 ; see also Friedrich et al, 2017 concerning these difficulties). In the present article we used the electrophysiological data to estimate precisely the eye dominance influence on IHTT for each direction of information exchange.…”

Section: Introductionmentioning

confidence: 83%

Interhemispheric Transfer Time Asymmetry of Visual Information Depends on Eye Dominance: An Electrophysiological Study

2018

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The interhemispheric transfer of information is a fundamental process in the human brain. When a visual stimulus appears eccentrically in one visual-hemifield, it will first activate the contralateral hemisphere but also the ipsilateral one with a slight delay due to the interhemispheric transfer. This interhemispheric transfer of visual information is believed to be faster from the right to the left hemisphere in right-handers. Such an asymmetry is considered as a relevant fact in the context of the lateralization of the human brain. We show here using current source density (CSD) analyses of visually evoked potential (VEP) that, in right-handers and, to a lesser extent in left-handers, this asymmetry is in fact dependent on the sighting eye dominance, the tendency we have to prefer one eye for monocular tasks. Indeed, in right-handers, a faster interhemispheric transfer of visual information from the right to left hemisphere was observed only in participants with a right dominant eye (DE). Right-handers with a left DE showed the opposite pattern, with a faster transfer from the left to the right hemisphere. In left-handers, albeit a smaller number of participants has been tested and hence confirmation is required, only those with a right DE showed an asymmetrical interhemispheric transfer with a faster transfer from the right to the left hemisphere. As a whole these results demonstrate that eye dominance is a fundamental determinant of asymmetries in interhemispheric transfer of visual information and suggest that it is an important factor of brain lateralization.

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“…We implemented the visual Poffenberger paradigm on a 61 cm widescreen (60 Hz refreshing rate), in line with the literature ( Westerhausen et al, 2006 ; Whitford et al, 2011 ; Friedrich et al, 2017 ; Chaumillon et al, 2018 ). The experiment was administered using Psychtoolbox-3.0.16 in MATLAB (R2019b, The Mathworks, Natick, MA).…”

Section: Methodsmentioning

confidence: 99%

“…MRI and EEG have been jointly used in brain connectivity studies and for the combined study of brain structure and function ( Westerhausen et al, 2006 ; Sui et al, 2014 ; Helbling et al, 2015 ; Horowitz et al, 2015 ; Deslauriers-Gauthier et al, 2019 ). In particular, the high temporal resolution of EEG allows the estimation of the interhemispheric transfer time (IHTT) ( Saron and Davidson, 1989 ; Marzi, 1999 ) using the established visual Poffenberger paradigm ( Westerhausen et al, 2006 ; Whitford et al, 2011 ; Friedrich et al, 2017 ; Chaumillon et al, 2018 ). Subsequently, an estimate of axonal conduction velocity can be computed ( Caminiti et al, 2013 ; Horowitz et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data

et al. 2022

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PurposeWe present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity.TheoryThe proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β.MethodsMRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract.ResultsThe estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm−α.ConclusionThe estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.

show abstract

Long-term reliability of the visual EEG Poffenberger paradigm

Cited by 17 publications

References 42 publications

In-vivo estimation of axonal morphology from MRI and EEG data

In-vivo estimation of axonal morphology from MRI and EEG data

Interhemispheric Transfer Time Asymmetry of Visual Information Depends on Eye Dominance: An Electrophysiological Study

In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data

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