Biomedical subjects

Fumikazu Miwakeichi

Publications and source records attributed to Fumikazu Miwakeichi.

5 recordsLinked to original sources

PubMed2005

High frequency activities in the human orbitofrontal cortex in sleep-wake cycle.

We recorded human orbitofrontal electrocorticogram during wakefulness and sleep in epileptic patients using subdural electrodes. During wakefulness and rapid eye movement (REM) sleep, we observed beta activity in the raw orbitofrontal signals. Power spectral analysis demonstrated beta enhancement during wakefulness and REM sleep when compared to slow wave sleep (SWS). During the phasic REM periods, the beta power was significantly lower than during the tonic REM periods. Gamma enhancement manifested itself in four out of six subjects during the phasic periods. This study is the first that has focused on electrical activity in the human orbitofrontal cortex. Although the role of the orbitofrontal cortex during sleep still remains unclear, high frequency activities give us important suggestions in elucidating the human sleep mechanism.

Adult↗

PubMed2004

Theta oscillation in the human anterior cingulate cortex during all-night sleep: an electrocorticographic study.

Ten epileptic patients each with subdural electrodes surgically attached to the anterior cingulate cortex (ACC; two cases), the orbitofrontal cortex (OFC; seven cases), or both (one case) were included in this study. We recorded each patient's ACC or OFC electrocorticogram (ECoG) during the time period that the patient was awake and naturally asleep. We performed a Fast Fourier Transformation (FFT) power spectral analysis on each ECoG to examine its frequency component. We found that the ACC showed regular and continuous theta oscillation (5-7Hz) during wakefulness and rapid eye movement (REM) sleep, but not during slow wave sleep. Theta waves observed in REM sleep were not as distinct as those found in wakefulness. We also discovered that the orbitofrontal signals represented spectral peaks in the theta band only during wakefulness. This suggests the coexistence of theta oscillation in the ACC. Considering our previous observations of gamma and beta oscillations in the human hippocampus, we hypothesize that the human limbic system manifests two oscillatory activities. The results obtained in this study suggest that electrophysiological activity in the ACC could be related to particular psychological functions in wakefulness and in REM sleep. These results are useful in elucidating the human brain mechanism.

Adult↗

PubMed2004

Concurrent EEG/fMRI analysis by multiway Partial Least Squares.

Data may now be recorded concurrently from EEG and functional MRI, using the Simultaneous Imaging for Tomographic Electrophysiology (SITE) method. As yet, there is no established means to integrate the analysis of the combined data set. Recognizing that the hemodynamically convolved time-varying EEG spectrum, S, is intrinsically multidimensional in space, frequency, and time motivated us to use multiway Partial Least-Squares (N-PLS) analysis to decompose EEG (independent variable) and fMRI (dependent variable) data uniquely as a sum of "atoms". Each EEG atom is the outer product of spatial, spectral, and temporal signatures and each fMRI atom the product of spatial and temporal signatures. The decomposition was constrained to maximize the covariance between corresponding temporal signatures of the EEG and fMRI. On all data sets, three components whose spectral peaks were in the theta, alpha, and gamma bands appeared; only the alpha atom had a significant temporal correlation with the fMRI signal. The spatial distribution of the alpha-band atom included parieto-occipital cortex, thalamus, and insula, and corresponded closely to that reported by Goldman et al. [NeuroReport 13(18) (2002) 2487] using a more conventional analysis. The source reconstruction from EEG spatial signature showed only the parieto-occipital sources. We interpret these results to indicate that some electrical sources may be intrinsically invisible to scalp EEG, yet may be revealed through conjoint analysis of EEG and fMRI data. These results may also expose brain regions that participate in the control of brain rhythms but may not themselves be generators. As of yet, no single neuroimaging method offers the optimal combination of spatial and temporal resolution; fusing fMRI and EEG meaningfully extends the spatio-temporal resolution and sensitivity of each method.

Brain↗

PubMed2004

Decomposing EEG data into space-time-frequency components using Parallel Factor Analysis.

Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the time-varying spectrum of multichannel EEG recordings--in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.

Alpha Rhythm↗

PubMedDate not supplied

Impulse response function based on multivariate AR model can differentiate focal hemisphere in temporal lobe epilepsy.

The purpose of this study is to propose and investigate a new approach for discriminating between focal and non-focal hemispheres in intractable temporal lobe epilepsy, based on applying multivariate time series analysis to the discharge-free background brain activity observed in nocturnal electrocorticogram (ECoG) time series. Five unilateral focal patients and one bilateral focal patient were studied. In order to detect the location of epileptic foci, linear multivariate autoregressive (MAR) models were fitted to the ECoG data; as a new approach for the purpose of summarizing these models in a single relevant parameter, the behavior of the corresponding impulse response functions was studied and described by an attenuation coefficient. In the majority of unilateral focal patients, the averaged attenuation coefficient was found to be almost always significantly larger in the focal hemisphere, as compared to the non-focal hemisphere. Also the amplitude of the fluctuations of the attenuation coefficient was significantly larger in the focal hemisphere. Moreover, in one patient showing a typical regular sleep cycle, the attenuation coefficient in the focal hemisphere tended to be larger during REM sleep and smaller during Non-REM sleep. In the bilateral focal patient, no statistically significant distinction between the hemispheres was found. This study provides encouraging results for new investigations of brain dynamics by multivariate parametric modeling. It opens up the possibility of relating diseases like epilepsy to the properties of inconspicuous background brain dynamics, without the need to record and analyze epileptic seizures or other evidently pathological waveforms.

Adult↗