Mingxiong Huang

A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG.

The spherical head model has been used in magnetoencephalography (MEG) as a simple forward model for calculating the external magnetic fields resulting from neural activity. For more realistic head shapes, the boundary element method (BEM) or similar numerical methods are used, but at greatly increased computational cost. We introduce a sensor-weighted overlapping-sphere (OS) head model for rapid calculation of more realistic head shapes. The volume currents associated with primary neural activity are used to fit spherical head models for each individual MEG sensor such that the head is more realistically modelled as a set of overlapping spheres, rather than a single sphere. To assist in the evaluation of this OS model with BEM and other head models, we also introduce a novel comparison technique that is based on a generalized eigenvalue decomposition and accounts for the presence of noise in the MEG data. With this technique we can examine the worst possible errors for thousands of dipole locations in a realistic brain volume. We test the traditional single-sphere model, three-shell and single-shell BEM, and the new OS model. The results show that the OS model has accuracy similar to the BEM but is orders of magnitude faster to compute.

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.

Commonalities and Differences Among Vectorized Beamformers in Electromagnetic Source Imaging

A number of beamformers have been introduced to localize neuronal activity using magnetoencephalography (MEG) and electroencephalography (EEG). However, currently available information about the major aspects of existing beamformers is incomplete. In the present study, detailed analyses are performed to study the commonalities and differences among vectorized versions of existing beamformers in both theory and practice. In addition, a novel beamformer based on higher-order covariance analysis is introduced. Theoretical formulas are provided on all major aspects of each beamformer; to examine their performance, computer simulations with different levels of correlation and signal-to-noise ratio are studied. Then, an empirical data set of human MEG median-nerve responses with a large number of neuronal generators is analyzed using the different beamformers. The results show substantial differences among existing MEG/EEG beamformers in their ways of describing the spatial map of neuronal activity. Differences in performance are observed among existing beamformers in terms of their spatial resolution, false-positive background activity, and robustness to highly correlated signals. Superior performance is obtained using our novel beamformer with higher-order covariance analysis in simulated data. Excellent agreement is also found between the results of our beamformer and the known neurophysiology of the median-nerve MEG response.

Multi-start downhill simplex method for spatio-temporal source localization in magnetoencephalography

A multi-start downhill simplex method is examined as a global minimization technique for fitting multidipole, spatio-temporal magnetoencephalography (MEG) data. This procedure has been performed on both simulated and empirical human visual data, known to exhibit complex field patterns due to multiple sources. Unlike some other non-linear fitting techniques the multi-start downhill simplex method does not require users to provide initial guesses for the dipole parameters, hence the fitting procedure is less time-consuming, more objective, and user-friendly. In addition, this method offers more than one adequate solution thus providing a range of uncertainty for the estimated parameters. The Multi-start downhill simplex method is used to fit the non-linear dipole spatial parameters, while the linear temporal parameters are fit using a separate linear fitting procedure. Singular value decomposition (SVD) is also used in order to improve the procedure for determining the adequate number of modeled dipoles.

Magnetoencephalography reveals early activation of V4 in grapheme-color synesthesia

Grapheme-color synesthesia is a neurological phenomenon in which letters and numbers (graphemes) consistently evoke particular colors (e.g. A may be experienced as red). The cross-activation theory proposes that synesthesia arises as a result of cross-activation between posterior temporal grapheme areas (PTGA) and color processing area V4, while the disinhibited feedback theory proposes that synesthesia arises from disinhibition of pre-existing feedback connections. Here we used magnetoencephalography (MEG) to test whether V4 and PTGA activate nearly simultaneously, as predicted by the cross-activation theory, or whether V4 activation occurs only after the initial stages of grapheme processing, as predicted by the disinhibited feedback theory. Using our high-resolution MEG source imaging technique (VESTAL), PTGA and V4 regions of interest (ROIs) were separately defined, and activity in response to the presentation of achromatic graphemes was measured. Activation levels in PTGA did not significantly differ between synesthetes and controls (suggesting similar grapheme processing mechanisms), whereas activation in V4 was significantly greater in synesthetes. In synesthetes, PTGA activation exceeded baseline levels beginning 105-109ms, and V4 activation did so 5ms later, suggesting nearly simultaneous activation of these areas. Results are discussed in the context of an updated version of the cross-activation model, the cascaded cross-tuning model of grapheme-color synesthesia.

M50 sensory gating predicts negative symptoms in schizophrenia.

Impaired auditory sensory gating is considered characteristic of schizophrenia and a marker of the information processing deficit inherent to that disorder. Predominance of negative symptoms also reflects the degree of deficit in schizophrenia and is associated with poorer pre-morbid functioning, lower IQ, and poorer outcomes. However, a consistent relationship between auditory sensory gating and negative symptoms in schizophrenia has yet to be demonstrated. The absence of such a finding is surprising, since both impaired auditory gating and negative symptoms have been linked with impaired fronto-temporal cortical function. The present study measured auditory gating using the P50 event related potential (ERP) in a paired-click paradigm and capitalized on the relative localization advantage of magnetoencephalography (MEG) to assess auditory sensory gating in terms of the event related field (ERF) M50 source dipoles on bilateral superior temporal gyrus (STG). The primary hypothesis was that there would be a positive correlation between lateralized M50 auditory sensory gating measures and negative symptoms in patients with schizophrenia. A standard paired-click paradigm was used during simultaneous EEG and MEG data collection to determine S2/S1 sensory gating ratios in a group of 20 patients for both neuroimaging techniques. Participants were administered the Schedule for the Assessment of Negative Symptoms (SANS), the Positive and Negative Symptom Scale (PANSS), and the Calgary Depression Scale for Schizophrenia. Consistent with previous reports, there was no relationship between ERP P50 sensory gating and negative symptoms. However, right (not left) hemisphere ERF M50 sensory gating ratio was significantly and positively correlated with negative symptoms. This finding is compatible with information processing theories of negative symptoms and with more recent findings of fronto-temporal abnormality in patients with predominantly negative symptoms.

Multistart Algorithms for MEG Empirical Data Analysis Reliably Characterize Locations and Time Courses of Multiple Sources

We applied our newly developed Multistart algorithm (M. Huang et al., 1998, Electroencephalogr. Clin. Neurophysiol. 108, 32-44) to high signal-to-noise ratio (SNR) somatosensory responses and low SNR visual data to demonstrate the reliability of this analysis tool for determining source locations and time courses of empirical multisource neuromagnetic data. This algorithm performs a downhill simplex search hundreds to thousands of times with multiple, randomly selected initial starting parameters from within the head volume, in order to avoid problems of local minima. Two subjects participated in two studies: (1) somatosensory (left and right median nerves were stimulated using a square wave pulse of 0.2 ms duration) and (2) visual (small black and white bulls-eye patterns were presented to central and peripheral locations in four quadrants of the visual field). One subject participated in both of the studies mentioned above and in a third study (i.e., simultaneous somatosensory/visual stimulation). The best-fitting solutions were tightly clustered in high SNR somatosensory data and all dominant regions of activity could be identified in some instances by using a single model order (e.g., six dipoles) applied to a single interval of time (e.g., 15-250 ms) that captured the entire somatosensory response. In low SNR visual data, solutions were obtained from several different model orders and time intervals in order to capture the dominant activity across the entire visual response (e.g. , 60-300 ms). Our results demonstrate that Multistart MEG analysis procedures can localize multiple regions of activity and characterize their time courses in a reliable fashion. Sources for visual data were determined by comparing results across several different models, each of which was based on hundreds to thousands of different fits to the data.

Sources on the anterior and posterior banks of the central sulcus identified from magnetic somatosensory evoked responses using Multi-Start Spatio-Temporal localization

A Multi‐Start Spatio‐Temporal (MSST) multidipole localization algorithm was used to study sources on the anterior and posterior banks of the central sulcus localized from early somatosensory magnetoencephalography (MEG) responses. Electrical stimulation was applied to the right and left median nerves of 8 normal subjects. Two sources, one on the anterior and one on the posterior bank of the central sulcus, were localized from 16 data sets (8 subjects, 2 hemispheres). Compared with the more traditional practice of single‐dipole fits to peak latencies, MSST provided more reliable source locations. The temporal dynamics of the anterior and posterior central sulcus sources, obtained using MSST, showed considerable temporal overlap. In some cases, the two sources appeared synchronous. On the other hand, in the traditional single‐dipole peak‐latency fit approach, there is no time course other than a focal dipole moment activated only at the selected peak latency. The same group of subjects also performed a motor task involving index‐finger lifting; the anterior central sulcus source obtained from electrical median nerve stimulation localized to the same or similar region in the primary motor area identified from the finger‐lift task. The physiological significance of the anterior central sulcus source is discussed. The findings suggest that one can test the integrity of cortical tissue in the region of primary motor cortex using electrical somatosensory stimulation. Hum. Brain Mapping 11:59–76, 2000.

Predicting EEG responses using MEG sources in superior temporal gyrus reveals source asynchrony in patients with schizophrenia

OBJECTIVE An integrated analysis using Electroencephalography (EEG) and magnetoencephalography (MEG) is introduced to study abnormalities in early cortical responses to auditory stimuli in schizophrenia. METHODS Auditory responses were recorded simultaneously using EEG and MEG from 20 patients with schizophrenia and 19 control subjects. Bilateral superior temporal gyrus (STG) sources and their time courses were obtained using MEG for the 30-100 ms post-stimulus interval. The MEG STG source time courses were used to predict the EEG signal at electrode Cz. RESULTS In control subjects, the STG sources predicted the EEG Cz recording very well (97% variance explained). In schizophrenia patients, the STG sources accounted for substantially (86%) and significantly (P<0.0002) less variance. After MEG-derived STG activity was removed from the EEG Cz signal, the residual signal was dominated by 40 Hz activity, an indication that the remaining variance in EEG is probably contributed by other brain generators, rather than by random noise. CONCLUSIONS Integrated MEG and EEG analysis can differentiate patients and controls, and suggests a basis for a well established abnormality in the cortical auditory response in schizophrenia, implicating a disorder of functional connectivity in the relationship between STG sources and other brain generators.

A Novel Integrated MEG and EEG Analysis Method for Dipolar Sources

The ability of magnetoencephalography (MEG) to accurately localize neuronal currents and obtain tangential components of the source is largely due to MEGs insensitivity to the conductivity profile of the head tissues. However, MEG cannot reliably detect the radial component of the neuronal current. In contrast, the localization accuracy of electroencephalography (EEG) is not as good as MEG, but EEG can detect both the tangential and radial components of the source. In the present study, we investigated the conductivity dependence in a new approach that combines MEG and EEG to accurately obtain, not only the location and tangential components, but also the radial component of the source. In this approach, the source location and tangential components are obtained from MEG alone, and optimal conductivity values of the EEG model are estimated by best-fitting EEG signal, while precisely matching the tangential components of the source in EEG and MEG. Then, the radial components are obtained from EEG using the previously estimated optimal conductivity values. Computer simulations testing this integrated approach demonstrated two main findings. First, there are well-organized optimal combinations of the conductivity values that provide an accurate fit to the combined MEG and EEG data. Second, the radial component, in addition to the location and tangential components, can be obtained with high accuracy without needing to know the precise conductivity profile of the head. We then demonstrated that this new approach performed reliably in an analysis of the 20-ms component from human somatosensory responses elicited by electric median-nerve stimulation.