Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naı̈ve patients with schizophrenia: preliminary results

Koenig, Thomas; Lehmann, Dietrich; Saito, Naomi; Kuginuki, T; Kinoshita, Toshihiko; Koukkou, M.

doi:10.1016/s0920-9964(00)00154-7

Cited by 159 publications

(134 citation statements)

References 16 publications

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“…A previous study on GFS comparing acutely schizophrenic patients with healthy controls has shown that patients had reduced GFS values in the theta band, which has been related to the existing hypothesis of a disconnection of working memory processes in schizophrenia [22] .…”

Section: Within This Context a Novel Methods Called Global Field Syncmentioning

confidence: 82%

“…recently been introduced to measure functional synchronization in frequency-domain EEG data [22] . For the entire set of EEG channels and for a given frequency, a single GFS value is obtained representing the percentage of EEG activity that oscillated with a common phase.…”

Section: Within This Context a Novel Methods Called Global Field Syncmentioning

confidence: 99%

“…The computation of GFS has been described in detail in Koenig et al [22] ; and is PC exceeds the eigenvalue of the second PC, the more the endpoints approximate a straight line. If the first and the second PC are nearly equal, the vectors' end points are scattered.…”

Section: Gfs Computationmentioning

confidence: 99%

“…If the first and the second PC are nearly equal, the vectors' end points are scattered. The GFS value is thus determined as that percentage of the total power that can maximally be explained by a single phase [22] . GFS ranges from 0 to 1, high GFS values indicate the presence of a single, predominant phase over all electrodes, Decreased EEG synchronization in Alzheimer's disease and mild cognitive impairment whereas low GFS indicate the absence of such a common phase.…”

Section: Gfs Computationmentioning

confidence: 99%

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Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment

Koenig

Prichep²,

Dierks

et al. 2005

Neurobiology of Aging

475

355

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The hypothesis of a functional disconnection of neuro-cognitive networks in patients with mild cognitive impairment (MCI) and Alzheimer Dementia was investigated using baseline resting EEG data. EEG databases from New York (264 subjects) and Stockholm (155 subjects), including healthy controls and patients with varying degrees of cognitive decline or Alzheimer Dementia were analyzed using Global Field Synchronization (GFS), a novel measure of global EEG synchronization. GFS reflects the global amount of phase-locked activity at a given frequency by a single number; it is independent of the recording reference and of implicit source models. Patients showed decreased GFS values in Alpha, Beta and Gamma frequency bands, and increased GFS values in the Delta band, confirming the hypothesized disconnection syndrome. The results are discussed within the framework of current knowledge about the functional significance of the affected frequency bands.

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Section: Within This Context a Novel Methods Called Global Field Syncmentioning

confidence: 82%

Section: Within This Context a Novel Methods Called Global Field Syncmentioning

confidence: 99%

Section: Gfs Computationmentioning

confidence: 99%

Section: Gfs Computationmentioning

confidence: 99%

See 2 more Smart Citations

Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment

Koenig

Prichep²,

Dierks

et al. 2005

Neurobiology of Aging

475

355

View full text Add to dashboard Cite

show abstract

“…An alternative phase synchrony index is Global Field Synchronization (GFS) (Koenig et al, 2001); whereas the index γ (22) is determined for pairs of signals x and y, GFS quantifies the synchrony of multiple signals…”

Section: Phase Synchronymentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

371

384

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It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as Mild Cognitive Impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic-event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish AD patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

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Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

Murta

Leite

Carmichael³

et al. 2014

Hum. Brain Mapp.

132

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Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG–fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological–haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG–fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG–fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG–fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations.

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Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naı̈ve patients with schizophrenia: preliminary results

Cited by 159 publications

References 16 publications

Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment

Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

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