Since September of 2005, we've helped thousands of people with our state-of-the-art technology. We are one of only about 20 health care centers in the nation to perform magnetoencephalography (MEG), a non-invasive, painless and quiet technique for measuring the very weak magnetic fields in the brain, associated with the brain's electrical activity.

MEG is the most advanced and accurate functional brain imaging technique currently available. We utilize MEG data, advanced data analytics tools, and machine learning to pre-surgically locate the sources of epileptic seizures to improve surgical outcomes of patients, pre-surgically locate the eloquent cortex in patients undergoing brain surgery for trauma or epilepsy, determine whether a patient is suffering from TBI or post-traumatic brain injury (PTSD), map brain networks to study PTSD and other brain diseases, and conduct research on the efficacies of medications for schizophrenia, along with many other applications.


Roland Lee, MD

UCSD Chief of Neuroradiology
Professor of Radiology
Director of MEG

Mingxiong Huang, PhD

Professor of Radiology
Co-Director of MEG

Annemarie Angeles

Staff Research Associate

Ashley Robb Swan

Staff Research Associate

Zhengwei Ji

Staff Research Associate

Qian Shen, PhD

Project Scientist

Michelle Spurdens

Hospital Assistant

Tao Song

Research Scientist

Ericka Foote

Research Associate


Marked Increases in Resting-State MEG Gamma-Band Activity in Combat-Related Mild Traumatic Brain Injury
Cerebral Cortex
May 2019

Combat-related mild traumatic brain injury (mTBI) is a leading cause of sustained impairments in military service members and veterans. Recent animal studies show that GABA-ergic parvalbumin-positive interneurons are susceptible to brain injury, with damage causing abnormal increases in spontaneous gamma-band (30–80 Hz) activity. This is the first human study to demonstrate abnormal resting-state gamma activity in mTBI. These novel findings suggest the possibility that abnormal gamma activities may be a proxy for GABA-ergic interneuron dysfunction and a promising neuroimaging marker of insidious mild head injuries.

Figure 1. Group differences in gamma-band resting-state MEG activity. Hyperactivity (red-yellow color) and hypoactivity (blue-cyan color) in gamma-band resting-state MEG source imaging in individuals with mTBI, compared with healthy controls. The Z coordinates in MNI-152 standard space are displayed for the images +58 to −27 with 5 mm gaps. The 9 magenta arrows indicate representative areas in which gamma activity was significantly correlated with neuropsychological test scores; see Figures 2 and 3. Images are displayed in radiological view.

MEG Working Memory N-Back Task Reveals Functional Deficits in Combat-Related Mild Traumatic Brain Injury
Cerebral Cortex
May 2019

Compared with healthy combat controls, mTBI participants showed increased MEG signals across frequency bands in frontal pole (FP), ventromedial prefrontal cortex, orbitofrontal cortex (OFC), and anterior dorsolateral prefrontal cortex (dlPFC), but decreased MEG signals in anterior cingulate cortex. Hyperactivations in FP, OFC, and anterior dlPFC were associated with slower reaction times. MEG activations in lateral FP also negatively correlated with performance on tests of letter sequencing, verbal fluency, and digit symbol coding. The profound hyperactivations from FP suggest that FP is particularly vulnerable to combat-related mTBI.

Figure 4. MEG activations correlate with reaction time measures. Columns 1 and 2 in the left panel of show that MEG WM activations correlate with RT measure during 1-back task. Columns 3 and 4 in the left panel show the MEG-RT correlation result during 2-back task. Right panel: 3 representative scatter plots showing significant positive correlations between MEG activation and RT for mTBI (red stars) and control subjects (blue circles). The plots are for the 3 areas (a), (b), and (c) in which the locations are indicated by white arrows in the left panel.

High-resolution MEG source imaging approach to accurately localize Broca’s area in patients with brain tumor or epilepsy
Clinical Neurophysiology
Feb 2016

Localizing expressive language function has been challenging using the conventional magnetoencephalography (MEG) source modeling methods. The present MEG study presents a new accurate and precise approach in localizing the language areas using a high-resolution MEG source imaging method. The present study demonstrates that using Fast-VESTAL, MEG can serve as an accurate and reliable functional imaging tool for presurgical mapping of language functions in patients with brain tumors and/or epilepsies.

Figure 3. Single-subject-based MEG localization of Broca’s area (green crossing hairs) in two subjects with large left frontal lobe tumors during object-naming task using Fast- VESTAL. The settings of the statistical significance in this figure are the same as in Fig. 2. The MEG data from these two patients with large tumors were not used in further group analyses as the linear affine transformation failed to correctly register the MRIs of these two patients to the MNI-152 atlas.

Voxel-wise resting-state MEG source magnitude imaging study reveals neurocircuitry abnormality in active-duty service members and veterans with PTSD
Aug 2014

Post-traumatic stress disorder (PTSD) is a leading cause of sustained impairment, distress, and poor quality of life in military personnel, veterans, and civilians. The present study showed thatMEGsource 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.

Figure 3. Top panel: abnormal alpha band (8–12 Hz)MEG activity in PTSD; bottom panel: abnormal low-frequency band (1–7 Hz) MEG activity in PTSD. The t-threshold of 2.9 is associated with FDR corrected p b .05.

MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images
Sep 2013

The present study developed a fastMEG 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. Additionally, in simulations and cases withMEG human responses, the results obtained fromusing conventional beamformer technique were compared with those from Fast-VESTAL, which highlighted the beamformer's problems of signal leaking and distorted source time-courses.

Figure 4. Cross correlation coefficient matrix for the 6 simulated source. (A): using ground-truth source time-courses; (B): using time-courses reconstructed by Fast-VESTAL at white-noise Level 1; (C): by Standard-VESTAL; (D): by beamformer. The coefficients under the lower-left white triangles were used to calculate the inter-source cross correlation (ICC) and their percent variance explained to the ground-truth values, as listed in Table 1.