On the Effect of Volume Conduction on Graph Theoretic Measures of Brain Networks in Epilepsy

Christodoulakis, Manolis; Hadjipapas, Avgis; Papathanasiou, Eleftherios S.; Anastasiadou, Maria; Papacostas, Savvas; Mitsis, Georgios D.

doi:10.1007/7657_2013_65

Cited by 25 publications

(42 citation statements)

References 30 publications

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“…close to one for correlation or coherence) yielding disconnected graphs, or too low (close to zero), yielding densely/fully connected graphs. For threshold values between these two extremes the graphs exhibited similar properties, as shown in (Christodoulakis et al 2013) for shorter data segments, and in supplementary material ( Fig. A.1 ; however, they were found to yield rather densely connected networks and the results were similar to those obtained by simply setting a fixed, low threshold value.…”

Section: Network Binarizationsupporting

confidence: 70%

“…The threshold value was selected such that networks with similar average degrees were obtained for different correlation measures. For the three examined correlation measures (as well as alternative ones -see (Christodoulakis et al 2013), it was found that different threshold values yielded very similar results in terms of the time evolution and the corresponding periodicity patterns for all network measures. This was found to be the case except when the threshold value was too high (e.g.…”

Section: Network Binarizationmentioning

confidence: 84%

“…Subsequently, the data was converted to the bipolar montage, as this montage was found to be more robust to volume conduction effects in the present case, where a limited number of electrodes was available (Christodoulakis et al 2013). According to this montage, pairs of EEG electrodes placed in nearby locations of the scalp are used to obtain the timeseries by subtracting the corresponding measurements, forming the pairs Fp1-F7, F7-T3, T3-T5, T5-O1, Fp2-F8, F8-T4, T4-T6, T6-O2, Fp1-F3, F3-C3, C3-P3, P3-O1, Fp2-F4, F4-C4, C4-P4, P4-O2, Fz-Cz and Cz-Pz.…”

Section: Eeg Recording and Preprocessingmentioning

confidence: 98%

“…Cross-correlation often takes its maximum at zero lag in the case of scalp EEG measurements. Consistent zero-lag correlations could be due to volume conduction effects, whereby currents from underlying sources are conducted instantaneously through the head volume to the EEG sensors (i.e., assuming that scalp potentials have no delays compared to their underlying sources (quasi-static approximation) (Nunez and Srinivasan 2006;Christodoulakis et al 2013). In principle, true direct interactions between any two physiological sources will typically incur a nonzero delay due to transmission speed, provided that the sampling frequency is high enough to capture such delays.…”

Section: Corrected Cross-correlationmentioning

confidence: 99%

“…(Kuhnert et al 2010;Baud et al 2018). To make sure that our analysis were not biased by the well-known EEG volume conduction effects (Nunez and Srinivasan 2006), we used the bipolar montage (Christodoulakis et al 2013) and considered three correlation measures (correlation, corrected cross-correlation and coherence) that are differentially sensitive to volume conduction and reference choice effects. In general, while zero-lag correlations could be due to both artefactual (volume conduction/reference effects) and true correlations, non-zero lag correlations are more likely to reflect true correlations of underlying sources ).…”

Section: Study Limitationsmentioning

confidence: 99%

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Multi-scale periodicities in the functional brain networks of patients with epilepsy and their effect on seizure detection

Mitsis

Anastasiadou

Christodoulakis

et al. 2017

Preprint

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Highlights-We have examined the long-term characteristics of EEG functional brain networks and their correlations to seizure onset -We show periodicities over multiple time scales in network summative properties (degree, efficiency, clustering coefficient) -We also show that, in addition to average network properties, the same periodicities exist in network topology using a novel measure (graph edit distance), suggesting that specific connectivity patterns recur over time -These periodic patterns were preserved when we corrected for the effects of volume conduction and were found to be of much larger magnitude compared to seizureinduced modulations -For the first time to our knowledge, we demonstrate that seizure onset occurs preferentially at specific phases of the network periodic components, particularly for shorter periodicities (around 3 and 5 hours) -These correlations were nearly absent when examining univariate properties (EEG signal power), suggesting that network-based measures rather than EEG signal-based measures are more tightly coupled with seizure onset -Our findings suggest that seizure detection and prediction algorithms may benefit significantly by taking into account these longer-term variations in brain network properties -As we show strong evidence that shorter network-based periodicities (3-5 hours) are tightly coupled with seizure onset, our results pave the way for further investigation into the pathophysiology of seizure generation mechanisms beyond the well-known effects of circadian rhythms All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/221036 doi: bioRxiv preprint first posted online 3 AbstractThe task of automated epileptic seizure detection and prediction, by using non-invasive measurements such as scalp EEG signals or invasive, intracranial recordings, has been at the heart of epilepsy studies for at least three decades. By far, the most common approach for tackling this problem is to examine short-length recordings around the occurrence of a seizure -normally ranging between several seconds and up to a few minutes before and after the epileptic event -and identify any significant changes that occur before or during the event. An inherent assumption in these studies is the presence of a relatively constant EEG activity in the interictal period, which is presumably interrupted by the occurrence of a seizure. Here, we examine this assumption by using long-duration scalp EEG data (ranging between 21 and 94 hours) in patients with epilepsy, based on which we construct functional brain networks. Our results suggest that not only these networks vary over time, but they do so in a periodic fashion, exhibiting multiple periods ranging between around one and 24 hours...

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Section: Network Binarizationsupporting

confidence: 70%

Section: Network Binarizationmentioning

confidence: 84%

Section: Eeg Recording and Preprocessingmentioning

confidence: 98%

Section: Corrected Cross-correlationmentioning

confidence: 99%

Section: Study Limitationsmentioning

confidence: 99%

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Multi-scale periodicities in the functional brain networks of patients with epilepsy and their effect on seizure detection

Mitsis

Anastasiadou

Christodoulakis

et al. 2017

Preprint

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Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease

Moguilner,

Berezuk,

Bender

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONSleep disturbances are common in Alzheimer's disease (AD) and may reflect pathologic changes in brain networks. To date, no studies have examined changes in sleep functional connectivity (FC) in AD or their relationship with network hyperexcitability and cognition.METHODSWe assessed electroencephalogram (EEG) sleep FC in 33 healthy controls, 36 individuals with AD without epilepsy, and 14 individuals with AD and epilepsy.RESULTSAD participants showed increased gamma connectivity in stage 2 sleep (N2), which was associated with longitudinal cognitive decline. Network hyperexcitability in AD was associated with a distinct sleep connectivity signature, characterized by decreased N2 delta connectivity and reversal of several connectivity changes associated with AD. Machine learning algorithms using sleep connectivity features accurately distinguished diagnostic groups and identified “fast cognitive decliners” among study participants who had AD.DISCUSSIONOur findings reveal changes in sleep functional networks associated with cognitive decline in AD and may have implications for disease monitoring and therapeutic development.Highlights Brain functional connectivity (FC) in Alzheimer's disease is altered during sleep. Sleep FC measures correlate with cognitive decline in AD. Network hyperexcitability in AD has a distinct sleep connectivity signature.

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The effects of dexamphetamine on the resting‐state electroencephalogram and functional connectivity

Albrecht

Roberts

Price

et al. 2015

Human Brain Mapping

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The catecholamines-dopamine and noradrenaline-play important roles in directing and guiding behavior. Disorders of these systems, particularly within the dopamine system, are associated with several severe and chronically disabling psychiatric and neurological disorders. We used the recently published group independent components analysis (ICA) procedure outlined by Chen et al. (2013) to present the first pharmaco-EEG ICA analysis of the resting-state EEG in healthy participants administered 0.45 mg/kg dexamphetamine. Twenty-eight healthy participants between 18 and 41 were recruited. Bayesian nested-domain models that explicitly account for spatial and functional relationships were used to contrast placebo and dexamphetamine on component spectral power and several connectivity metrics. Dexamphetamine led to reductions across delta, theta, and alpha spectral power bands that were predominantly localized to Frontal and Central regions. Beta 1 and beta 2 power were reduced by dexamphetamine at Frontal ICs, while beta 2 and gamma power was enhanced by dexamphetamine in posterior regions, including the parietal, occipital-temporal, and occipital regions. Power-power coupling under dexamphetamine was similar for both states, resembling the eyes open condition under placebo. However, orthogonalized measures of power coupling and phase coupling did not show the same effect of dexamphetamine as power-power coupling. We discuss the alterations of low- and high-frequency EEG power in response to dexamphetamine within the context of disorders of dopamine regulation, in particular schizophrenia, as well as in the context of a recently hypothesized association between low-frequency power and aspects of anhedonia. Hum Brain Mapp 37:570-588, 2016. © 2015 Wiley Periodicals, Inc.

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On the Effect of Volume Conduction on Graph Theoretic Measures of Brain Networks in Epilepsy

Cited by 25 publications

References 30 publications

Multi-scale periodicities in the functional brain networks of patients with epilepsy and their effect on seizure detection

Multi-scale periodicities in the functional brain networks of patients with epilepsy and their effect on seizure detection

Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease

The effects of dexamphetamine on the resting‐state electroencephalogram and functional connectivity

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