D. Khosla

Separation of veins from activated brain tissue in functional magnetic resonance images at 1.5T

A feasibility study was conducted to segment 1.5T functional magnetic resonance images (fMRIs) into gray matter and veins using individual pixel intensity difference and temporal phase delay as two correlated parameters in 1.5T gradient echo images. The time-course of each pixel in gradient echo images acquired during visual stimulation with a checkerboard flashing at 8 Hz was correlated to the stimulation on-off sequence to identify activated pixels, and the temporal delay of each activated pixel was computed by fitting its time-course to a reference sinusoidal function. A histogram of the product of each pixels intensity (in the activated image) and corresponding temporal delay could be fitted to a bimodal distribution, which was then used to segment the functional image into veins and activated brain tissue. The results show relatively good demarcation between large veins and activated gray matter using this method. >

Combining functional MRI and EEG source imaging

Even though the entire data for multislice imaging in functional magnetic resonance imaging (fMRI) can be acquired in 50-100 ms using echo-planar imaging, the effective temporal resolution for studying brain function is limited to 1-2s due to the relatively slow temporal response of the underlying hemodynamics. The objective of the preliminary study reported here was to combine evoked potential measurements (commonly referred to as EEG) with fMRI of the same subject to improve temporal resolution. EEG and 1.5T fMRI data were acquired from the subjects during checkerboard stimulation. The EEG data were recorded on a standard 10-20 electrode placement system and sources were reconstructed using a 4-sphere model based on the anatomical MRI of the subjects. Simulated annealing based algorithms were developed to estimate single equivalent dipole and multiple dipole parameters for both single time-point and spatio-temporal EEG data. The results from both single and multiple dipole estimations indicate good correlation between the dipole locations and fMRI.<<ETX>>

Toward correlating functional MRI and EEG sources

Though excellent spatial resolution (on the order of 1 mm) is obtainable in functional MRI (fMRI), its temporal resolution is limited to about 1 second by hemodynamics. On the other hand, magnetoencephalography (MEG) and electroencephalography (EEG) provide millisecond temporal resolution but a relatively crude (on the order of 1 cm) spatial resolution to localized sources. Thus, techniques that could combine the high temporal resolution of MEG or EEG with the high spatial resolution of fMRI would be of great significance in imaging the spatiotemporal distribution of neuronal activation. With the ultimate objective of combining fMRI and EEG activation studies, we have conducted experiments to determine how pixels activated in fMRI correlate with underlying EEG sources in a given subject during visual stimulation. Results of a three-subject study suggest good correlation between the center-of-gravity of activated pixels seen in fMRI and the center-of-gravity of regions localized through EEG measurements.

Mapping brain activity in gradient-echo functional MRI using principal component analysis

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Effect of phase encoding steps on 1D chemical shift imaging of lactate during brain activation

Lactate is a unique indicator of brain activation and is detectable in vivo by proton magnetic resonance spectroscopy. Previous brain activation studies have been confined to single-voxel localization of lactate. To extend this work to 1D chemical shift imaging, computer simulation, test-object and human studies were conducted to examine tradeoffs among the number of phase encoding steps, signal-to-noise ratio (SNR) and resolution. An iterative algorithm was developed to reduce truncation artifacts arising from a limited number of phase encoding steps. The results indicate that the resolution and SNR attained with 8 phase encoding steps and 16 averages per step after applying the truncation reduction algorithm are approximately equal to those attained with 32 encoding steps and 4 averages per step. Thus, 32 steps are preferred since contamination is minimized with increasing steps. A human study with 32 phase encodings showed a factor of two increase in lactate in the right auditory cortex during left-ear tonal stimulation. >