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Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild (and some moderate) TBI can be difficult to diagnose because the injuries are often not detectable on conventional MRI or CT. Injured brain tissues in TBI patients generate abnormal low-frequency magnetic activity (ALFMA, peaked at 1–4 Hz) that can be measured and localized by magnetoencephalography (MEG). We developed a new automated MEG low-frequency source imaging method and applied this method in 45 mild TBI (23 from combat-related blasts, and 22 from non-blast causes) and 10 moderate TBI patients (non-blast causes). Seventeen of the patients with mild TBI from blasts had tertiary injuries resulting from the blast. The results show our method detected abnormalities at the rates of 87% for the mild TBI group (blast-induced plus non-blast causes) and 100% for the moderate group. Among the mild TBI patients, the rates of abnormalities were 96% and 77% for the blast and non-blast TBI groups, respectively. The spatial characteristics of abnormal slow-wave generation measured by Z scores in the mild blast TBI group significantly correlated with those in non-blast mild TBI group. Among 96 cortical regions, the likelihood of abnormal slow-wave generation was less in the mild TBI patients with blast than in the mild non-blast TBI patients, suggesting possible protective effects due to the military helmet and armor. Finally, the number of cortical regions that generated abnormal slow-waves correlated significantly with the total post-concussive symptom scores in TBI patients. This study provides a foundation for using MEG low-frequency source imaging to support the clinical diagnosis of TBI.

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.

Minimum L1-norm solutions have been used by many investigators to analyze MEG responses because they provide high spatial resolution images. However, conventional minimum L1-norm approaches suffer from instability in spatial construction, and poor smoothness of the reconstructed source time-courses. Activity commonly “jumps” from one grid point to (usually) the neighboring grid points. Equivalently, the time-course of one specific grid point can show substantial “spiky-looking” discontinuity. In the present study, we present a new vector-based spatial–temporal analysis using a L1-minimum-norm (VESTAL). This approach is based on a principle of MEG physics: the magnetic waveforms in sensor-space are linear functions of the source time-courses in the imaging-space. Our computer simulations showed that VESTAL provides good reconstruction of the source amplitude and orientation, with high stability and resolution in both the spatial and temporal domains. “Spiky-looking” discontinuity was not observed in the source time-courses. Importantly, the simulations also showed that VESTAL can resolve sources that are 100% correlated. We then examined the performance of VESTAL in the analysis of human median-nerve MEG responses. The results demonstrated that this method easily distinguishes sources very spatially close to each other, including individual primary somatosensory areas (BA 1, 2, 3b), primary motor area (BA 4), and other regions in the somatosensory system (e.g., BA 5, 7, SII, SMA, and temporal–parietal junction) with high temporal stability and resolution. VESTAL's potential for obtaining information on source extent was also examined.

Previous studies using functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) of the brain have found that a distributed parietal-frontal neuronal network is activated in normals during both auditory and visual oddball tasks. The common cortical regions in this network are inferior parietal lobule (IPL)/supramarginal gyrus (SMG), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). It is not clear whether the same network is activated by oddball tasks during somatosensory stimulation. The present study addressed this question by testing healthy adults as they performed a novel median-nerve oddball paradigm while undergoing magnetoencephalography (MEG). An automated multiple dipole analysis technique, the Multi-Start Spatio-Temporal (MSST) algorithm, localized multiple neuronal generators, and identified their time-courses. IPL/SMG, ACC, and DLPFC were reliably localized in the MEG median-nerve oddball responses, with IPL/SMG activation significantly preceding ACC and DLPFC activation. Thus, the same parietal-frontal neuronal network that shows activation during auditory and visual oddball tests is activated in a median-nerve oddball paradigm. Regions uniquely related to somatosensory oddball responses (e.g., primary and secondary somatosensory, dorsal premotor, primary motor, and supplementary motor areas) were also localized. Since the parietal-frontal network supports attentional allocation during performance of the task, this study may provide a novel method, as well as normative baseline data, for examining attention-related deficits in the somatosensory system of patients with neurological or psychiatric disorders.

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.

Auditory information communicated through vocalizations, music, or sounds in the environment is commonly used to orient and direct attention to different locations in extrapersonal space. The neural networks subserving attention to auditory space remain poorly understood in comparison to our knowledge about attention in the visual system. The present study investigated whether a parietal-prefrontal right-hemisphere network controls endogenous orienting and reorienting of attention to the location of sounds just as it does for visual-spatial information. Seventeen healthy adults underwent event-related functional magnetic resonance imaging (FMRI) while performing an endogenous auditory orienting task, in which peripheral cues correctly (valid) or incorrectly (invalid) specified the location of a forthcoming sound. The results showed that a right precuneus and bilateral temporal-frontal network mediated the reorienting of auditory attention at both short and long stimulus onset asynchronies (SOAs). In contrast, the more automatic stage of auditory reorienting at the shorter SOA was associated with activation in a bilateral inferior parietal-frontal oculomotor network. These findings suggest that the reorienting of auditory attention is generally supported by a similar inferior parietal-frontal network as visual attention, but in both hemispheres. However, peripheral auditory cues also appear to elicit an automatic orienting response to the spatial location of a sound followed by a period of reduced processing of information that occurs in the same location later in time.