Voxel-wise resting-state MEG source magnitude imaging study reveals neurocircuitry abnormality in active-duty service members and veterans with PTSD.

Post-traumatic stress disorder (PTSD) is a leading cause of sustained impairment, distress, and poor quality of life in military personnel, veterans, and civilians. Indirect functional neuroimaging studies using PET or fMRI with fear-related stimuli support a PTSD neurocircuitry model that includes amygdala, hippocampus, and ventromedial prefrontal cortex (vmPFC). However, it is not clear if this model can fully account for PTSD abnormalities detected directly by electromagnetic-based source imaging techniques in resting-state. The present study examined resting-state magnetoencephalography (MEG) signals in 25 active-duty service members and veterans with PTSD and 30 healthy volunteers. In contrast to the healthy volunteers, individuals with PTSD showed: (1) hyperactivity from amygdala, hippocampus, posterolateral orbitofrontal cortex (OFC), dorsomedial prefrontal cortex (dmPFC), and insular cortex in high-frequency (i.e., beta, gamma, and high-gamma) bands; (2) hypoactivity from vmPFC, Frontal Pole (FP), and dorsolateral prefrontal cortex (dlPFC) in high-frequency bands; (3) extensive hypoactivity from dlPFC, FP, anterior temporal lobes, precuneous cortex, and sensorimotor cortex in alpha and low-frequency bands; and (4) in individuals with PTSD, MEG activity in the left amygdala and posterolateral OFC correlated positively with PTSD symptom scores, whereas MEG activity in vmPFC and precuneous correlated negatively with symptom score. The present study showed that MEG source imaging technique revealed new abnormalities in the resting-state electromagnetic signals from the PTSD neurocircuitry. Particularly, posterolateral OFC and precuneous may play important roles in the PTSD neurocircuitry model.

Single-subject-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mild traumatic brain injury.

Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild TBI (mTBI) can be difficult to detect using conventional MRI or CT. Injured brain tissues in mTBI patients generate abnormal slow-waves (1-4 Hz) that can be measured and localized by resting-state magnetoencephalography (MEG). In this study, we develop a voxel-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mTBI on a single-subject basis. A normative database of resting-state MEG source magnitude images (1-4 Hz) from 79 healthy control subjects was established for all brain voxels. The high-resolution MEG source magnitude images were obtained by our recent Fast-VESTAL method. In 84 mTBI patients with persistent post-concussive symptoms (36 from blasts, and 48 from non-blast causes), our method detected abnormalities at the positive detection rates of 84.5%, 86.1%, and 83.3% for the combined (blast-induced plus with non-blast causes), blast, and non-blast mTBI groups, respectively. We found that prefrontal, posterior parietal, inferior temporal, hippocampus, and cerebella areas were particularly vulnerable to head trauma. The result also showed that MEG slow-wave generation in prefrontal areas positively correlated with personality change, trouble concentrating, affective lability, and depression symptoms. Discussion is provided regarding the neuronal mechanisms of MEG slow-wave generation due to deafferentation caused by axonal injury and/or blockages/limitations of cholinergic transmission in TBI. This study provides an effective way for using MEG slow-wave source imaging to localize affected areas and supports MEG as a tool for assisting the diagnosis of mTBI.

MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images.

The present study developed a fast MEG source imaging technique based on Fast Vector-based Spatio-Temporal Analysis using a L1-minimum-norm (Fast-VESTAL) and then used the method to obtain the source amplitude images of resting-state magnetoencephalography (MEG) signals for different frequency bands. The Fast-VESTAL technique consists of two steps. First, L1-minimum-norm MEG source images were obtained for the dominant spatial modes of sensor-waveform covariance matrix. Next, accurate source time-courses with millisecond temporal resolution were obtained using an inverse operator constructed from the spatial source images of Step 1. Using simulations, Fast-VESTAL's performance was assessed for its 1) ability to localize multiple correlated sources; 2) ability to faithfully recover source time-courses; 3) robustness to different SNR conditions including SNR with negative dB levels; 4) capability to handle correlated brain noise; and 5) statistical maps of MEG source images. An objective pre-whitening method was also developed and integrated with Fast-VESTAL to remove correlated brain noise. Fast-VESTAL's performance was then examined in the analysis of human median-nerve MEG responses. The results demonstrated that this method easily distinguished sources in the entire somatosensory network. Next, Fast-VESTAL was applied to obtain the first whole-head MEG source-amplitude images from resting-state signals in 41 healthy control subjects, for all standard frequency bands. Comparisons between resting-state MEG sources images and known neurophysiology were provided. Additionally, in simulations and cases with MEG human responses, the results obtained from using conventional beamformer technique were compared with those from Fast-VESTAL, which highlighted the beamformer's problems of signal leaking and distorted source time-courses.

Resting-state neuronal oscillatory correlates of working memory performance.

PURPOSE: Working memory (WM) represents the brain's ability to maintain information in a readily available state for short periods of time. This study examines the resting-state cortical activity patterns that are most associated with performance on a difficult working-memory task. METHODS: Magnetoencephalographic (MEG) band-passed (delta/theta (1-7 Hz), alpha (8-13 Hz), beta (14-30 Hz)) and sensor based regional power was collected in a population of adult men (18-28 yrs, n = 24) in both an eyes-closed and eyes-open resting state. The normalized power within each resting state condition as well as the normalized change in power between eyes closed and open (zECO) were correlated with performance on a WM task. The regional and band-limited measures that were most associated with performance were then combined using singular value decomposition (SVD) to determine the degree to which zECO power was associated with performance on the three-back verbal WM task. RESULTS: Changes in power from eyes closed to open revealed a significant decrease in power in all band-widths that was most pronounced in the posterior brain regions (delta/theta band). zECO right posterior frontal and parietal cortex delta/theta power were found to be inversely correlated with three-back working memory performance. The SVD evaluation of the most correlated zECO metrics then provided a singular measure that was highly correlated with three-back performance (r = -0.73, p<0.0001). CONCLUSION: Our results indicate that there is an association between WM performance and changes in resting-state power (right posterior frontal and parietal delta/theta power). Moreover, an SVD of the most associated zECO measures produces a composite resting-state metric of regional neural oscillatory power that has an improved association with WM performance. To our knowledge, this is the first investigation that has found that changes in resting state electromagnetic neural patterns are highly associated with verbal working memory performance.

An automatic MEG low-frequency source imaging approach for detecting injuries in mild and moderate TBI patients with blast and non-blast causes.

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.

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

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.

Integrated imaging approach with MEG and DTI to detect mild traumatic brain injury in military and civilian patients.

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 due to lack of obvious external injuries and because the injuries are often not visible on conventional acute MRI or CT. Injured brain tissues in TBI patients generate pathological low-frequency neuronal magnetic signal (delta waves 1-4 Hz) that can be measured and localized by magnetoencephalography (MEG). We hypothesize that abnormal MEG delta waves originate from gray matter neurons that experience de-afferentation due to axonal injury to the underlying white matter fiber tracts, which is manifested on diffusion tensor imaging (DTI) as reduced fractional anisotropy. The present study used a neuroimaging approach integrating findings of magnetoencephalography (MEG) and diffusion tensor imaging (DTI), evaluating their utility in diagnosing mild TBI in 10 subjects in whom conventional CT and MRI showed no visible lesions in 9. The results show: (1) the integrated approach with MEG and DTI is more sensitive than conventional CT and MRI in detecting subtle neuronal injury in mild TBI; (2) MEG slow waves in mild TBI patients originate from cortical gray matter areas that experience de-afferentation due to axonal injuries in the white matter fibers with reduced fractional anisotropy; (3) findings from the integrated imaging approach are consistent with post-concussive symptoms; (4) in some cases, abnormal MEG delta waves were observed in subjects without obvious DTI abnormality, indicating that MEG may be more sensitive than DTI in diagnosing mild TBI.