Brain source montages improve the non-invasive diagnosis in epilepsy

Scherg, Michael; Bast, Thomas; Ille, Nicole; Weckesser, Dieter; Bornfleth, Harald; Berg, Patrick

doi:10.1016/j.ics.2004.04.035

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Hence, standard source spaces covering the whole brain are required to separate the activities of the different regions (16, 26, 37). As shown in Figures 10, 11 and previously (20, 26, 37–39), source montages provide a more focal view, approximate localization and reduction of overlap from other sources. Thus, abnormal signals like IEDs can be recognized more easily.…”

Section: Discussionmentioning

confidence: 57%

“…Attempts to take the EEG review beyond the scalp and cortical surface into the brain have been rare but successful by using so-called source montages (20, 26, 30, 36–39). As documented in these papers and illustrated in Figures 6, 9–12, 15, standard source spaces based on MDS models can (a) render the signals of focal brain activities more precisely, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, source activities are not smoothed and there is no cross-talk between sources, because applying TN-1 from the left onto T N produces the identity matrix, i.e. each signal is rendered maximally while spread from the other sources is suppressed (20).…”

Section: Methodsmentioning

confidence: 99%

“…Using simple linear algebra, the pseudo-inverse of the L-matrix is used as linear operator to transform the scalp data matrix D into the brain source wave matrix S. This macroscopic transformation is stable and unique because the number of signals is condensed from 12 into 4. The 4 inverse vectors have an important property: They render 100% of the source signal they represent, but 0% of the other sources (20). Thus, the first source waveform shows the N19-P30 component of the intact AC, while the second source waveform reveals the loss of activity in the deafferented AC, because there is no cross-talk from dipole source 1 to the location of any of the other 3 sources and vice versa.…”

Section: Transformation Into Source Space: Aep and Sep As Teaching Exmentioning

confidence: 99%

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Taking the EEG Back Into the Brain: The Power of Multiple Discrete Sources

Scherg¹,

Berg²,

Nakasato

et al. 2019

Front. Neurol.

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Background: In contrast to many neuroimaging modalities, clinical interpretation of EEG does not take advantage of post-processing and digital signal analysis. In most centers, EEG is still interpreted at sensor level, exactly as half a century ago. A major task in clinical EEG interpretation is the identification of interictal epileptiform discharges (IEDs). However, due to the overlap of background activity, IEDs can be hard to detect in the scalp EEG. Since traditional montages, like bipolar and average reference, are linear transformations of the recorded channels, the question is whether we can provide linear transformations of the digital EEG to take it back into the brain, at least on a macroscopic level. The goal is to improve visibility of epileptiform activities and to separate out most of the overlap. Methods: Multiple discrete sources provide a stable linear inverse to transform the EEG into source space with little cross-talk between source regions. The model can be based on a few dipoles or regional sources, adapted to the individual EEG and MRI data, or on selected standard sources evenly distributed throughout the brain, e.g. below the 25 EEG standard electrodes. Results: Auditory and somatosensory evoked potentials serve as teaching examples to show how various source spaces can reveal the underlying source components including their loss or alteration due to lesions. Source spaces were able to reveal the propagation of source activities in frontal IEDs and the sequential activation of the major nodes of the underlying epileptic network in myoclonic epilepsy. The power of multiple discrete sources in separating the activities of different brain regions was also evident in the ongoing EEG of cases with frontal cortical dysplasia and bitemporal lobe epilepsy. The new source space 25 made IEDs more clearly visible over the EEG background signals. The more focal nature of source vs. scalp space was quantitatively confirmed using a new measurement of focality. Conclusion: Multiple discrete sources have the power to transform the EEG back into the brain by defining new EEG traces in source space. Using standard source space 25, these can provide for improved clinical interpretation of EEG.

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Section: Discussionmentioning

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Transformation Into Source Space: Aep and Sep As Teaching Exmentioning

confidence: 99%

See 2 more Smart Citations

Taking the EEG Back Into the Brain: The Power of Multiple Discrete Sources

Scherg¹,

Berg²,

Nakasato

et al. 2019

Front. Neurol.

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“…Afterwards, data epochs were averaged separately for correct and incorrect trials. In order to transform surface MEG into brain source activity, a source montage with 51 fixed regional dipoles in a homogeneous sphere was defined [ 69 ]. In the case of MEG a regional dipole consists of two perpendicular, fixed equivalent current dipoles describing the two tangential components of a source, while the radial component does not contribute to the magnetic field outside the volume conductor.…”

Section: Methodsmentioning

confidence: 99%

Hippocampal activity during the transverse patterning task declines with cognitive competence but not with age

et al. 2010

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BackgroundThe hippocampus is a brain region that is particularly affected by age-related morphological changes. It is generally assumed that a loss in hippocampal volume results in functional deficits that contribute to age-related cognitive decline. In a combined cross-sectional behavioural and magnetoencephalography (MEG) study we investigated whether hippocampal-associated neural current flow during a transverse patterning task - which requires learning relational associations between stimuli - correlates with age and whether it is modulated by cognitive competence.ResultsBetter performance in several tests of verbal memory, verbal fluency and executive function was indeed associated with higher hippocampal neural activity. Age, however, was not related to the strength of hippocampal neural activity: elderly participants responded slower than younger individuals but on average produced the same neural mass activity.ConclusionsOur results suggest that in non-pathological aging, hippocampal neural activity does not decrease with age but is rather related to cognitive competence.

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