Wiener Filter-Magnetoencephalography of Visual Cortical Activity

Kajihara, Seiji; Ohtani, Yoshio; Goda, Naokazu; Tanigawa, M.; Ejima, Yoshimichi; Toyama, Keisuke

doi:10.1023/b:brat.0000047333.10619.eb

Cited by 17 publications

(14 citation statements)

References 32 publications

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“…The performances of the VB estimates were compared with those of the Wiener filter (Dale et al, 2000;Kajihara et al, 2004;Phillips et al, 2002). The Wiener filter estimates were conducted as Eq.…”

Section: Meg With No Fmri Informationmentioning

confidence: 99%

“…Consequently, the fMRI active areas may include inactive current areas in the MEG time slice data, and the fMRI activity for the active current may be smeared by the temporal averaging. The recent approaches, such as the Wiener filter or the Bayesian method (Dale et al, 2000;Kajihara et al, 2004;Phillips et al, 2002;Schmidt et al, 1999), use the fMRI data as prior information on the source current variance by assuming that the current variances for fMRI active dipoles are large compared with those for fMRI inactive dipoles. Source current estimation based on these methods may fail if the fMRI data contain incorrect information.…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the prior assumptions are still insufficient to fully resolve the ill-posed nature of the inverse problem, and thus the spatial resolution of these methods is still rather low. Attempts have been made to overcome these limitations by supplementing the information of other imaging means (Ahlfors et al, 1999;Dale and Sereno, 1993;Dale et al, 2000;Fujimaki et al, 2002;Heinze et al, 1994;Kajihara et al, 2004;Liu et al, 1998;Phillips et al, 2002;Schmidt et al, 1999). The MRI image gives information on the position and orientation of the cortical dipoles, while fMRI provides topographical information on active dipoles.…”

Section: Introductionmentioning

confidence: 99%

“…The VB current estimate was performed in the same way as for the highresolution VB with MRI information, except for the introduction of fMRI signals into the hierarchical priors defined by Eq. (32) (see Appendix B.4).The performance of the VB estimates was also compared with that of the Wiener filter estimates (Dale et al, 2000;Kajihara et al, 2004;Phillips et al, 2002), using the same MRI and fMRI information. For the noiseless case, the Wiener filter estimate is given by Eq.…”

mentioning

confidence: 99%

“…The estimated currents were slightly smaller than the true source currents assumed in these cortical areas. The estimation gains for V1v, V2v, and VP currents were 0.95, 0.86, and 0.87, and RMSEs for V1v, V2v, and VP currents were 0.06, 0.21, and 0.12, respectively (Table 3).The performances of the VB estimates were compared with those of the Wiener filter (Dale et al, 2000;Kajihara et al, 2004;Phillips et al, 2002). The Wiener filter estimates were conducted as Eq.…”

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confidence: 99%

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Hierarchical Bayesian estimation for MEG inverse problem

Yoshioka²,

et al. 2004

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Source current estimation from MEG measurement is an ill-posed problem that requires prior assumptions about brain activity and an efficient estimation algorithm. In this article, we propose a new hierarchical Bayesian method introducing a hierarchical prior that can effectively incorporate both structural and functional MRI data. In our method, the variance of the source current at each source location is considered an unknown parameter and estimated from the observed MEG data and prior information by using the Variational Bayesian method. The fMRI information can be imposed as prior information on the variance distribution rather than the variance itself so that it gives a soft constraint on the variance. A spatial smoothness constraint, that the neural activity within a few millimeter radius tends to be similar due to the neural connections, can also be implemented as a hierarchical prior. The proposed method provides a unified theory to deal with the following three situations: (1) MEG with no other data, (2) MEG with structural MRI data on cortical surfaces, and (3) MEG with both structural MRI and fMRI data. We investigated the performance of our method and conventional linear inverse methods under these three conditions. Simulation results indicate that our method has better accuracy and spatial resolution than the conventional linear inverse methods under all three conditions. It is also shown that accuracy of our method improves as MRI and fMRI information becomes available. Simulation results demonstrate that our method appropriately resolves the inverse problem even if fMRI data convey inaccurate information, while the Wiener filter method is seriously deteriorated by inaccurate fMRI information. D 2004 Elsevier Inc. All rights reserved. Keywords: MEG/EEG inverse problem; Hierarchical Bayesian method;Variational Bayesian method; Variance estimation; MRI and fMRI information; Spatial smoothness constraint IntroductionFunctional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) are the major recording means of brain activity. fMRI records brain activity with millimeter-order spatial resolution, but its temporal resolution is on the order of several seconds due to slow hemodynamic responses to neural activity (Bandettini, 2000;Belliveau et al., 1991;Churchland and Sejnowski, 1988;Logothetis et al., 2001;Ogawa et al., 1990). Conversely, MEG measures brain activity with millisecond-order temporal resolution, but its spatial resolution is poor due to the ill-posed nature of the inverse problem for estimating source currents from the electromagnetic measurement (Nunez, 1981). In addition, the number of MEG sensors is generally insufficient to provide a precise reconstruction of the source current (Grave de Peralta Menendez and Gonzalez Andino, 1998). Therefore, prior information on the source currents is essential to solve the inverse problem. Inverse procedures are commonly classified as dipole and distributed source methods. The dipole method (Aine et al., 2000;Hari, 1991;Mosher et al., 19...

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Section: Meg With No Fmri Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Hierarchical Bayesian estimation for MEG inverse problem

Yoshioka²,

et al. 2004

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Triple Hyp→Pro replacement in integramide A, a peptaib inhibitor of HIV‐1 integrase: Effect on conformation and bioactivity

et al. 2011

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AIDS is produced by HIV-induced infections. HIV integrase is an important enzyme as it is critical for the integration of the HIV genome into that of the host cell. This complex process is exclusively carried out by a viral enzyme not found in the host cell. Therefore, this protein represents a safe target for the development of single or combined anti-HIV therapy. Integramide A is a 16-mer long, effective peptaib inhibitor of HIV-1 integrase. We have previously described a versatile synthetic strategy in solution to afford this natural compound and its diastereomer at positions 14 and 15. We also found that both peptides display a significant inhibitory activity. Here, we present our data on the synthesis in solution, in-depth conformational analysis, and biological activity against HIV-integrase of the analogs of the two above mentioned peptides in which all of the three (2S,4R)-Hyp residues at positions 2, 9, and 13 are replaced by L-Pro. This study definitely confirms that the mixed α-/3(10) - helical conformation of natural integramide A plays a key role in its mechanism of inhibition. Moreover, our data provide evidence that the amphipathic character of this helical structure is not required for the activity of integramide A against HIV-1 integrase. These observations will hopefully help us to further clarify the precise mechanism of inhibition of this interesting peptaib and to identify shorter peptide sequences active against HIV-1 integrase.

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Improved method for retinotopy constrained source estimation of visual‐evoked responses

Hagler¹,

Dale²

2011

Human Brain Mapping

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Retinotopy constrained source estimation (RCSE) is a method for non-invasively measuring the time courses of activation in early visual areas using magnetoencephalography (MEG) or electroencephalography (EEG). Unlike conventional equivalent current dipole or distributed source models, the use of multiple, retinotopically-mapped stimulus locations to simultaneously constrain the solutions allows for the estimation of independent waveforms for visual areas V1, V2, and V3, despite their close proximity to each other. We describe modifications that improve the reliability and efficiency of this method. First, we find that increasing the number and size of visual stimuli results in source estimates that are less susceptible to noise. Second, to create a more accurate forward solution, we have explicitly modeled the cortical point spread of individual visual stimuli. Dipoles are represented as extended patches on the cortical surface, which take into account the estimated receptive field size at each location in V1, V2, and V3 as well as the contributions from contralateral, ipsilateral, dorsal, and ventral portions of the visual areas. Third, we implemented a map fitting procedure to deform a template to match individual subject retinotopic maps derived from functional magnetic resonance imaging (fMRI). This improves the efficiency of the overall method by allowing automated dipole selection, and it makes the results less sensitive to physiological noise in fMRI retinotopy data. Finally, the iteratively reweighted least squares (IRLS) method was used to reduce the contribution from stimulus locations with high residual error for robust estimation of visual evoked responses.

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Wiener Filter-Magnetoencephalography of Visual Cortical Activity

Cited by 17 publications

References 32 publications

Hierarchical Bayesian estimation for MEG inverse problem

Hierarchical Bayesian estimation for MEG inverse problem

Triple Hyp→Pro replacement in integramide A, a peptaib inhibitor of HIV‐1 integrase: Effect on conformation and bioactivity

Improved method for retinotopy constrained source estimation of visual‐evoked responses

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