EEG analysis of epilepsy by directed coherence method a data processing approach

Takigawa, Morikuni; Wang, Gang; Kawasaki, Hirotoki; Fukuzako, Hiroshi

doi:10.1016/0167-8760(95)00046-1

Cited by 31 publications

(17 citation statements)

References 7 publications

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“…The spectral measure associated was called directed coherence (DCOH; Saito and Harashima 1981). The DCOH was widely used with EEG and auditory evoked potentials (AEP) data especially by Takigawa (1988) and his team for the studies of light therapy, connections between cerebral hemispheres , links between frontal and occipital cortices for alpha rythm (Wang and Yunokuchi 2002) and epileptic patients (Takigawa et al 1996), functional interactions between cortical areas for rats under methamphetamine , and influence of transcranial magnetic stimulation (Jing and Takigawa 2000;Jing et al 2001a,b).…”

Section: Linear Methodsmentioning

confidence: 98%

“…Some various methods have been explored to assess significance for these measures. Takigawa et al (1996) noticed that the logarithmic transformation log 10 10X/(1 − X) of their DCOH data approximated the normal distribution. They used it to perform Student's t test between two sets of patients.…”

Section: Statistical Significancementioning

confidence: 97%

“…It is often determined by criteria like Akaike's criterion (Schnider et al 1989;Takigawa et al 1996;Bernasconi and Konig 1999;Jing et al 2001b;Wang and Yunokuchi 2002) or by the visual fitting quality of the ARX when Akaike's criterion failed (Brovelli et al 2004;Guéguin et al 2006). …”

Section: Order Of the Arx Modelmentioning

confidence: 99%

“…These coefficients are generally estimated by solving Yule-Walker equations (Jing et al 2001b;Takigawa et al 1996;Takigawa et al 2000;Ljung 1999). We also chose this method for this paper.…”

Section: Modelmentioning

confidence: 99%

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Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

2006

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Section: Linear Methodsmentioning

confidence: 98%

Section: Statistical Significancementioning

confidence: 97%

Section: Order Of the Arx Modelmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

See 2 more Smart Citations

Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

2006

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“…The DCOH model is based on the assumption that two signals (Ch 1 -Ch 2 ) can be considered generated by an autoregressive model. The model and algorithm method are described elsewhere [50].…”

Section: Methods and Materials S Subjectsmentioning

confidence: 99%

Comparisons of event-related potentials after repetitive transcranial magnetic stimulation

Jing¹,

Takigawa²,

Okamura³

et al. 2001

Journal of Neurology

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Effects of repetitive transcranial magnetic stimulation (rTMS) on the human cognitive process were investigated by examining auditory event-related potentials (ERPs) in 15 healthy subjects. Two rTMS trains were delivered over the left frontal area, with 30 pulses in each train. ERPs were recorded at 14 electrode sites on the scalp using a typical oddball protocol before and after rTMS. Tone stimuli (20% target and 80% standard) were delivered through earphones. Latency and amplitude of N100, P200, N200 and P300 were measured and compared during the study. To observe information flow between two electrode sites, directed coherence (DCOH) was calculated on the ERPs. Our results show that the effect of rTMS differs in the various ERPs components (P < 0.001). The latency of P300 significantly increased after stimulation, and the increase was more obvious in the frontal (18.6 ms) and central (15.8 ms) areas. The latency of P200 decreased in all areas. The amplitude of component N100 in the frontal and central areas decreased after rTMS. DCOH from the central area to the temporal area and DCOH from the parietal area to the temporal area were significantly higher than the DCOH between other areas (P < 0.01), and these properties were not affected by rTMS (P > 0.05). Information flow was driven from the frontal area to the parietal area after stimulation. Our results suggest that rTMS can suppress cognitive activities, showing an inhibitory effect on neurophysiological processes in the human brain. Since the temporal area is located at the terminus of the propagation pathways, it plays important roles in processing information in cognitive activities.

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Dynamic changes in the direction of the theta rhythmic drive between supramammillary nucleus and the septohippocampal system

Kocsis

Kamiński

2006

Hippocampus

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Neurons in the supramammillary nucleus (SUM) of urethane-anesthetized rats fire rhythmically in synchrony with hippocampal theta rhythm. As these neurons project to the septum and hippocampus, it is generally assumed that their role is to mediate ascending activation, leading to the hippocampal theta rhythm. However, the connections between SUM and the septohippocampal system are reciprocal; there is strong evidence that theta remains in the hippocampus after SUM lesions and in the SUM after lesioning the medial septum. The present study examines the dynamics of coupling between rhythmic discharge in the SUM and hippocampal field potential oscillations, using the directionality information carried by the two signals. Using directed transfer function analysis, we demonstrate that during sensory-elicited theta rhythm and also during short episodes of theta acceleration of spontaneous oscillations, the spike train of a subpopulation of SUM neurons contains information predicting future variations in rhythmic field potentials in the hippocampus. In contrast, during slow spontaneous theta rhythm, it is the SUM spike signal that can be predicted from the preceding segment of the electrical signal recorded in the hippocampus. These findings indicate that, in the anesthetized rat, SUM neurons effectively drive theta oscillations in the hippocampus during epochs of sensory-elicited theta rhythm and short episodes of theta acceleration, whereas spontaneous slow theta in the SUM is controlled by descending input from the septohippocampal system. Thus, in certain states, rhythmically firing SUM neurons function to accelerate the septal theta oscillator, and in others, they are entrained by a superordinate oscillatory network.

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EEG analysis of epilepsy by directed coherence method a data processing approach

Cited by 31 publications

References 7 publications

Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

Linear and nonlinear causality between signals: methods, examples and neurophysiological applications

Comparisons of event-related potentials after repetitive transcranial magnetic stimulation

Dynamic changes in the direction of the theta rhythmic drive between supramammillary nucleus and the septohippocampal system

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