Dual-Core Beamformer for obtaining highly correlated neuronal networks in MEG

Volumetric imaging of brain activity with spatial-frequency decoding of neuromagnetic signals

“…Auditory sources are well-known "correlated sources" that are difficulty for the conventional beamformer and dipole fit to separate (Diwakar et al, 2011;Pang et al, 2003). We found that high-frequency signals were helpful in separating two sources in the brain for several reasons.…”

“…Building on recent reports (Blakely et al, 2014;Ding and Yuan, 2013;Gramfort et al, 2013), we hypothesize that the new method which utilizes the frequency and spectral signatures of multi-frequency MEG is superior to the conventional beamforming as well as widely used dipole modeling. In comparison to previous reports on similar approaches (Dalal et al, 2011;Diwakar et al, 2011;Prendergast et al, 2013;Zhang et al, 2013), the major innovation of the present study was the unitization of the frequency signatures for source localization and the optimization of wavelet-based beamformers for detecting weak high-frequency signals. Building on previous reports (Popescu et al, 2008), we have improved beamformers by combining PSC and coherent source region suppression (CSRC) into a new approach.…”

Spanning the rich spectrum of the human brain: slow waves to gamma and beyond

“…This allows us to not only examine the spatially distinct sources of cortical activity versus artifact, but to boost effective SNR as well (Sekihara et al 2004; Ward et al For even further sensitivity and generalizability, usually a large number of trials are acquired and statistics are computed across subjects. Indeed, these strategies seem to have the greatest success in resolving gamma activity at the scalp level (Dalal et al 2008;Muthukumaraswamy 2010;Dockstader et al 2010;Diwakar et al 2011). Further confirmation on the possibilities and limitations of scalp recordings can be obtained from occasional opportunities to record them simultaneously with intracranial EEG (Dalal et al 2008;Ball et al 2009;Litvak et al 2010;Rampp et al 2010).…”

Development of large-scale functional networks over the lifespan

“…A grid of 326 voxels (2 × 2 × 2 cm) that covered approximately the entire brain volume was used for the beamformer. Even though other beamforming techniques exist that are optimized for the detection of correlated sources (e.g., Diwakar et al, 2011) we decided to use the LCMV beamformer for this study because we were interested in the reconstruction of large-scale functional brain networks in source space rather than on 2 correlated sources. As suggested by Brookes and colleagues (Brookes et al, 2008) we used a broad frequency band of 1-100 Hz for the beamforming technique in order to minimize distortions of the source reconstruction, time course, and estimation of the signal strength.…”