Electrophysiological Neuroimaging
. Cortical Imaging Technique
Cortical potential maps are of clinical importance for the presurgical diagnosis in
epilepsy patients. Comparing with scalp potential, i.e. electroencephalogram
(EEG), it has the substantially increased spatial resolution, which, however, is
only available by means of invasive sub-dural electrode recordings, i.e.
electrocorticogram (ECoG) in a clinical setting. A novel cortical imaging
technique has been developed in our laboratory to estimate cortical potential
maps, which reflect well the cortical source distributions, directly from scalprecorded EEG signals in a realistic geometry inhomogeneous head model by
means of the boundary element method (He et al. 1999, pdf). This technique
provides a unique inverse solution to the EEG inverse problem and offers an
important capability of noninvasively estimating cortical potentials. The validity of
this technique has been rigorously validated in a group of epilepsy patients (He
et al. 2002, pdf). Recently, we further advanced the technique with the use of
finite element method, which enable us to incorporate highly inhomogeneous and
anisotropic media into our head model. We thus are able to further evaluate and
validate this technique with simultaneously recorded EEG and ECoG data
(Zhang et al., 2006, pdf) during the same session. Our cortical imaging results
demonstrate its capability of imaging cortical regions displaying epileptiform
activity as confirmed by neurosurgical treatment in the same patients (Zhang et
al. 2003, pdf).
. 3D Current Source Imaging and Connectivity Imaging
It is of importance to develop a high resolution spatio-temporal imaging modality
to directly image spatially distributed neuronal electrical current flow. EEG
provides unique insights into the dynamic behavior of the human brain as it has
the ability to follow changes in neural activity on a millisecond timescale. On the
other hand, raw head surface potential recording cannot provide the satisfactory
assessment and interpretation of brain function because of its comparatively low
spatial resolution and limited penetration. We have been developing a subspace
source localization approach - FINE, for localizing brain electrical current sources
in 3D brain space (Xu et al. 2004, pdf) with high spatial resolution, and further
advanced by considering the realistic geometry inhomogeneous head model
(Ding & He, 2006, pdf). Our current source imaging results have indicates its
applicability in localizing neural sources responsible for evoked potentials and
spontaneous activity (Ding et al., 2006, pdf) as supported by other imaging
modalities, such as MRI and ECoG. Furthermore, we have been developing
connectivity imaging techniques based on the outcomes from 3D current source
imaging to infer the dynamic behavior and functional relationship among the
current sources. One of such techniques integrated with FINE has demonstrates
its capability in distinguish neural sources for seizure initiation and those for
seizure propagation in epilepsy (Ding et al., 2006, pdf).
Multimodal Neuroimaging
. Experimental Multimodal Imaging
Dramatic advancements in brain research have been fueled by increasingly
sophisticated neuroimaging technology. Functional MRI (fMRI) using
endogenous blood oxygenation level dependent (BOLD) contrast is a well
established technique for mapping human brain function, which is being used
extensively in human neuroscience research and is rapidly entering clinical
practice. On the other hand, the benefits of EEG are almost precisely the
obverse of those of fMRI, in that it provides millisecond level temporal resolution
but with limited spatial resolution. We have been investigating the simultaneous
EEG and fMRI during the same experimental session and have demonstrated
the feasibility in a 3T MRI system. We have been investigating the effects of
simultaneous recording on EEG, fMRI, and EEG-fMRI integrated source imaging
results. The developed experimental multimodal imaging framework have greatly
facilitated our investigations on brain functions, such as the retinotopic mapping
in human visual system (Im et al, J Neurosci Meth, 2006, pdf) and the cortical
visual pathways and dynamic visual interactions.
. Multimodal Integration: EEG-fMRI Neuroimaging
The EEG and fMRI techniques are complementary instead of competitive in
functional neuroimaging considering their different emphases in spatial and time
domains. Experimental multimodal imaging is good at investigating neuroscience
problems. However, different imaging techniques in this framework are lack of
unified and consistent spatial and temporal responses and, thus usually provide
information in different contexts. Towards this end, we have developed a novel
algorithm based on the Twomey regularization to address the potential spatial
mismatches between the fMRI response and the EEG neural source activity (Liu
et al, Clin Neurophsiol, 2006, pdf). We have been investigating multimodal
integration techniques for EEG-fMRI neuroimaging. Such multimodal techniques
hold a unified theoretical basis and promise to combine the complementary
advantages of EEG and fMRI, as well, by providing high resolutions in both
temporal and spatial domains. A neuroimaging technique has been developed to
reconstruct cortical current source distribution based on cortical current density
(CCD) model by fusing EEG and fMRI measurements. With such CCD
reconstruction, we are able to analyze the complex spatiotemporal neural
dynamics and sophisticated neural interaction (Babiloni et al., 2005, pdf). The
availability of such high-resolution spatio-temporal functional neuroimaging
technology would provide an important advancement in brain research, and
improve clinical diagnosis and management of neurological and psychiatric
disorders.
Neural Interfacing
. Spatial-Time-Frequency Approach to Brain-Computer-Interface
Electroencephalography (EEG) is an important technique for studying the
temporal dynamics of neural activities and interactions. The state-of-the-art EEG
mapping includes a high-density array of sensors that record electrical potentials
over the scalp, giving rise to a spatiotemporal dataset. Our lab has developed a
variety of signal processing techniques to obtain the signature of brain activity
spanned in time, frequency and spatial domains (Wang & He, 2004, pdf).
Statistical approaches such as principal component analysis (PCA) and
independent component analysis (ICA) are used for denoising and isolating
signal sources from the raw EEG data. Spatial filtering technique such as
Laplacian operator allows the enhancement of spatial resolution of EEG mapping.
Time-frequency analysis using Morlet wavelet decomposition and short-time
Fourier transform has been demonstrated capable of characterizing event-related
(de)synchronization (ERD/ERS) associated with motor imagery tasks. We have
been investigating the exploitation of these ERD/ERS signals for high accuracy
pattern recognition and classification in the brain-computer-interface application
(Yamawaki & He, 2006, pdf). The high efficiency could be achieved by such
novel spatial-time-frequency approach through optimizing the information
extraction in multi-channel EEGs.
Figure 1. The evolution of a piece of raw EEG (first row) through a series of EEG
processing steps. Time 0 indicates the preparation cue onset moment. Values from 0 to 2
second were used for classification. Second row shows Laplacian filtered EEG signals.
Rhythmical power variations along time course are delineated by its envelopes (third
row), which constitutes an original feature description. By averaging these envelopes of
one mental state over all trials, clear ERD/ERS patterns can be observed (fourth row).
Fig 2 Time–frequency weight distributions of 9 subjects for the case montage IV
and QZ4. See detail in Wang & He, 2004.
. Inverse imaging in Brain-Computer-Interface
A Brain-Computer-Interface (BCI) is a method of communication based on
voluntary neural activity generated by the brain and independent of its normal
output pathways of peripheral nerves and muscles. The neural activity used in
BCI can be recorded using invasive or noninvasive techniques. EEG signals,
because of their relatively short time constants, are widely used in BCI systems.
BCI can provide the brain with a new, non-muscular communication and control
channel for conveying messages and commands to the external world. We have
been investigating new means of extracting useful information using from scalp
recorded single trials of EEG signals corresponding to motor imagery tasks
through neural source imaging techniques, which offers high sensitivity of
detecting the intent of human subject (Qin, Ding, & He, J of Neural Eng, 2004,
pdf; Kamousi & He, 2005, pdf). Such investigation on the source dimensions of
brain activity underlying BCI is promising to help us understanding its control
mechanism and possibly leads to the further development and advancement of
new BCI system.
Fig. 1 Schematic diagram of a BCI system (Vallabhaneni, Wang & He, 2005).
Figure 2 The upper two rows are examples of equivalent dipole solutions of different trials in a
human subject during imagination of left or right hand in α band. (A, B, C, D, E—left-hand
movement imagery; F, G, H, I, J—right-hand movement imagery.) The lower two rows are
examples of cortical current density distributions of different trials in a human subject during
motor imagination. (A, B, C, D, E—left-hand movement imagery; F, G, H, I, J—right-hand
movement imagery.) The results were estimated from single trial scalp EEG data.
Cardiac Electrical Imaging
. 3D Model-based Electrocardiac Tomography
It is of significance to noninvasively image cardiac electrical activity throughout
the three dimensional (3D) myocardium. The availability of such an
electrocardiac tomography technology may provide critically important
information for a better understanding of the mechanisms of cardiac arrhythmia,
and clinical diagnosis and management of a variety of cardiac disease. We have
developed 3D electrocardiac tomography techniques to image cardiac current
density distributions within the myocardium (He & Wu, 2001, pdf), and a heartmodel based tomographic imaging approach to localize the site of origin of
cardiac activation (Li & He, 2001, pdf; Li et al., 2003, pdf), image cardiac
activation sequence (He et al., 2002, pdf), and image the transmembrane
potential distribution (He et al., 2003, pdf) within the 3D anisotropic myocardium
in a realistic geometry inhomogeneous heart-torso model. We have been
conducting the validation studies of these imaging techniques on both animal
models (Zhang et al., 2005, pdf) and human subjects. The ultimate goal is to
develop cardiac functional imaging techniques which can image and localize
sites of arrhythmogenesis and their activation pattern.
Fig. 1 Schematic diagram of 3D electrocardiographic activation imaging.
ig. 2 An example of electrocardiographic localization in a patient with implanted
pacemaker. The tip of the pacing lead (red dot) was estimated (green dot) with a
localization error of 5.2 mm.
Fig. 1. A: measured body-surface potential maps following ventricular pacing at a right
ventricular site in rabbit experiment. Time refers to the time instant since pacing. B: 3-D AS
derived from intracardiac electrograms. Sections are arranged sequentially from base to midlevel
(1–11, top row) and from midlevel to apex (13–23, bottom row). C: 3-D AS estimated
noninvasively by means of 3-dimensional electrocardiographic imaging. See details in Zhang et
al., 2005, pdf
. 3D Inverse Cardiac Activation Imaging
We have also developed a novel ECG inverse approach for imaging the 3D
ventricular activation sequence, which is based on the estimation of the
equivalent current density throughout the entire volume of ventricular
myocardium (Liu et al, IEEE Trans Med Imaging, 2006, pdf). The spatio-temporal
coherence of ventricular excitation process has been utilized to derive the
activation time from the estimated time course of equivalent current density
defined as the spatial gradient of transmembrane potential. Our pilot results in a
computer simulation study have demonstrated the feasibility in imaging 3D
cardiac activation sequences. The new technique is promising to provide the
important clinical value as well as our 3D model-based electrocardiac
tomography
techniques.
Figure 2: Illustration of the proposed 3-D activation imaging approach.
See details in Liu et al, IEEE Trans Med Imaging, 2006.
Bioimpedance Imaging
Electrical impedance information is useful not only for characterizing biological
tissues, but also providing important information aiding electrical source imaging.
Novel means are being investigated to image electrical impedance distribution of
the biological tissues.
. Magnetoacoustic Tomography
We have developed a novel methodology -- magnetoacoustic tomography with
magnetic induction (MAT-MI) -- by integrating magnetism and ultrasound. MATMI generates magnetic stimulation and then measures ultrasound response for
bioimpedance imaging. In MAT-MI, the object is placed in a static magnetic field
and a pulsed magnetic field. The pulsed magnetic field induces eddy current in
the object. Consequently, the object will emit ultrasonic waves through the
Lorenz force produced by the combination of the eddy current and the static
magnetic field. The acoustic waves are then collected by the detectors located
around the object for image reconstruction. Theoretical (Xu & He, 2005, pdf) and
experimental (Li, Xu, He, 2006, pdf) studies indicate that MAT-MI promises to
provide high spatial resolution in imaging electrical impedance distribution. Our
simulation results further demonstrate the feasibility to reconstruct conductivity
distribution with great contrast without the sacrifice of spatial resolution (Li, Xu,
He, 2007, pdf). Our future research is undergoing on investigating its physical
mechanism and new experimental system, and developing novel image
reconstruction for MAT-MI.
. Magnetic Resonance Electrical Impedance Tomography (Gao et al 2006)
Magnetic Resonance Electrical Impedance Tomography (MREIT) is a newly
developed imaging modality that reconstructs the electrical impedance
distribution from the magnetic flux density generated by the injected current into
a volume conductor. In MREIT, a low frequency current is usually used through a
pair of electrodes, and its induced magnetic flux density can be measured by a
MRI scanner distributed over the entire conductive media. Such magnetic flux
distribution depends on the electrical impedance distribution of the media and,
thus, the electrical impedance tomography within the entire media can be
reconstructed noninvasively. We have investigating novel MREIT reconstruction
algorithms (Gao, Zhu & He, 2005, pdf; Gao, Zhu & He, 2006, pdf), which use
only one directional measurement of the magnetic flux density. Electrical
impedance distribution of biological tissues can provide useful information about
their physiological and pathological status, such as in breast cancer. The future
development of MREIT is to improve its sensitivity to small electrical impedance
changes and its robustness to measurement noises.