Research and Radiology Services

San Diego, CA, United States

Research and Radiology Services

San Diego, CA, United States
SEARCH FILTERS
Time filter
Source Type

Diwakar M.,University of California at San Diego | Huang M.-X.,University of California at San Diego | Huang M.-X.,Research and Radiology Services | Srinivasan R.,University of California at Irvine | And 10 more authors.
NeuroImage | Year: 2011

The "Dual-Core Beamformer" (DCBF) is a new lead-field based MEG inverse-modeling technique designed for localizing highly correlated networks from noisy MEG data. Conventional beamformer techniques are successful in localizing neuronal sources that are uncorrelated under poor signal-to-noise ratio (SNR) conditions. However, they fail to reconstruct multiple highly correlated sources. Though previously published dual-beamformer techniques can successfully localize multiple correlated sources, they are computationally expensive and impractical, requiring a priori information. The DCBF is able to automatically calculate optimal amplitude-weighting and dipole orientation for reconstruction, greatly reducing the computational cost of the dual-beamformer technique. Paired with a modified Powell algorithm, the DCBF can quickly identify multiple sets of correlated sources contributing to the MEG signal. Through computer simulations, we show that the DCBF quickly and accurately reconstructs source locations and their time-courses under widely varying SNR, degrees of correlation, and source strengths. Simulations also show that the DCBF identifies multiple simultaneously active correlated networks. Additionally, DCBF performance was tested using MEG data in humans. In an auditory task, the DCBF localized and reconstructed highly correlated left and right auditory responses. In a median-nerve stimulation task, the DCBF identified multiple meaningful networks of activation without any a priori information. Altogether, our results indicate that the DCBF is an effective and valuable tool for reconstructing correlated networks of neural activity from MEG recordings. © 2010 .


Diwakar M.,University of California at San Diego | Tal O.,University of California at San Diego | Liu T.T.,University of California at San Diego | Harrington D.L.,University of California at San Diego | And 9 more authors.
NeuroImage | Year: 2011

Beamformer spatial filters are commonly used to explore the active neuronal sources underlying magnetoencephalography (MEG) recordings at low signal-to-noise ratio (SNR). Conventional beamformer techniques are successful in localizing uncorrelated neuronal sources under poor SNR conditions. However, the spatial and temporal features from conventional beamformer reconstructions suffer when sources are correlated, which is a common and important property of real neuronal networks. Dual-beamformer techniques, originally developed by Brookes et al. to deal with this limitation, successfully localize highly-correlated sources and determine their orientations and weightings, but their performance degrades at low correlations. They also lack the capability to produce individual time courses and therefore cannot quantify source correlation. In this paper, we present an enhanced formulation of our earlier dual- core beamformer (DCBF) approach that reconstructs individual source time courses and their correlations. Through computer simulations, we show that the enhanced DCBF (eDCBF) consistently and accurately models dual-source activity regardless of the correlation strength. Simulations also show that a multi-core extension of eDCBF effectively handles the presence of additional correlated sources. In a human auditory task, we further demonstrate that eDCBF accurately reconstructs left and right auditory temporal responses and their correlations. Spatial resolution and source localization strategies corresponding to different measures within the eDCBF framework are also discussed. In summary, eDCBF accurately reconstructs source spatio-temporal behavior, providing a means for characterizing complex neuronal networks and their communication. © 2011.


Tal O.,University of California at San Diego | Diwakar M.,University of California at San Diego | Wong C.-W.,University of California at San Diego | Olafsson V.,University of California at San Diego | And 4 more authors.
Frontiers in Human Neuroscience | Year: 2013

In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity. However, because the BOLD signal is an indirect measure of neuronal activity, its complex hemodynamic nature can complicate the interpretation of differences in connectivity that are observed across conditions or subjects. For example, prior studies have shown that caffeine leads to widespread reductions in BOLD connectivity but were not able to determine if neural or vascular factors were primarily responsible for the observed decrease. In this study, we used source-localized magnetoencephalography (MEG) in conjunction with fMRI to further examine the origins of the caffeine-induced changes in BOLD connectivity. We observed widespread and significant (p < 0.01) reductions in both MEG and fMRI connectivity measures, suggesting that decreases in the connectivity of resting-state neuro-electric power fluctuations were primarily responsible for the observed BOLD connectivity changes. The MEG connectivity decreases were most pronounced in the beta band. By demonstrating the similarity in MEG and fMRI based connectivity changes, these results provide evidence for the neural basis of resting-state fMRI networks and further support the potential of MEG as a tool to characterize resting-state connectivity. © 2013 Tal, Diwakar, Wong, Olafsson, Lee, Huang and Liu.

Loading Research and Radiology Services collaborators
Loading Research and Radiology Services collaborators