Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm.
نویسندگان
چکیده
This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity.
منابع مشابه
Corrigendum to “Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm”
[This corrects the article DOI: 10.1155/2016/4890497.].
متن کاملMEG Source Localization Using Invariance of Noise Space
We propose INvariance of Noise (INN) space as a novel method for source localization of magnetoencephalography (MEG) data. The method is based on the fact that modulations of source strengths across time change the energy in signal subspace but leave the noise subspace invariant. We compare INN with classical MUSIC, RAP-MUSIC, and beamformer approaches using simulated data while varying signal-...
متن کاملComparison of SAM and MUSIC performance for unaveraged MEG
SAM (synthetic aperture magnetometry) and MUSIC (multiple signal classification) are methods for identifying dipolar sources represented in the covariance of MEG measurements. SAM operates by estimating source and noise power, as a function of position and current vector, from a full-rank covariance matrix [1]. MUSIC finds the locations for which a test dipole is orthogonal to the covariance no...
متن کاملRobust Methods for Reconstructing Brain Activity and Functional Connectivity from MEG Data
The synchronous brain activity measured via mangentoencephalography (MEG) arises from current dipoles located throughout the cortex. Estimating the number, location, timecourse, and orientation of these dipoles, called sources, remains a challenging task, one that is significantly compounded by the effects of source correlations and interference from spontaneous brain activity and sensor noise....
متن کاملA Signal-Processing Pipeline for Magnetoencephalography Resting-State Networks
To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented by means of a dedicated processing pipeline: MEG recordings are decomposed by independent component analysis (ICA) into artifact and brain components (ICs); next, the chann...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Neural plasticity
دوره 2016 شماره
صفحات -
تاریخ انتشار 2016