John W. Belliveau

High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity

Magnetic resonance (MR) diffusion tensor imaging (DTI) can resolve the white matter fiber orientation within a voxel provided that the fibers are strongly aligned. However, a given voxel may contain a distribution of fiber orientations due to, for example, intravoxel fiber crossing. The present study sought to test whether a geodesic, high b‐value diffusion gradient sampling scheme could resolve multiple fiber orientations within a single voxel. In regions of fiber crossing the diffusion signal exhibited multiple local maxima/minima as a function of diffusion gradient orientation, indicating the presence of multiple intravoxel fiber orientations. The multimodality of the observed diffusion signal precluded the standard tensor reconstruction, so instead the diffusion signal was modeled as arising from a discrete mixture of Gaussian diffusion processes in slow exchange, and the underlying mixture of tensors was solved for using a gradient descent scheme. The multitensor reconstruction resolved multiple intravoxel fiber populations corresponding to known fiber anatomy. Magn Reson Med 48:577–582, 2002.

Dynamic Statistical Parametric Mapping: Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity

Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.

Functional neuroanatomy of biological motion perception in humans

We used whole brain functional MRI to investigate the neural network specifically engaged in the recognition of “biological motion” defined by point-lights attached to the major joints and head of a human walker. To examine the specificity of brain regions responsive to biological motion, brain activations obtained during a “walker vs. non-walker” discrimination task were compared with those elicited by two other tasks: (i) non-rigid motion (NRM), involving the discrimination of overall motion direction in the same “point-lights” display, and (ii) face-gender discrimination, involving the discrimination of gender in briefly presented photographs of men and women. Brain activity specific to “biological motion” recognition arose in the lateral cerebellum and in a region in the lateral occipital cortex presumably corresponding to the area KO previously shown to be particularly sensitive to kinetic contours. Additional areas significantly activated during the biological motion recognition task involved both, dorsal and ventral extrastriate cortical regions. In the ventral regions both face-gender discrimination and biological motion recognition elicited activation in the lingual and fusiform gyri and in the Brodmann areas 22 and 38 in superior temporal sulcus (STS). Along the dorsal pathway, both biological motion recognition and non-rigid direction discrimination gave rise to strong responses in several known motion sensitive areas. These included Brodmann areas 19/37, the inferior (Brodmann Area 39), and superior parietal lobule (Brodmann Area 7). Thus, we conjecture that, whereas face (and form) stimuli activate primarily the ventral system and motion stimuli primarily the dorsal system, recognition of biological motion stimuli may activate both systems as well as their confluence in STS. This hypothesis is consistent with our findings in stroke patients, with unilateral brain lesions involving at least one of these areas, who, although correctly reporting the direction of the point-light walker, fail on the biological motion task.

Conductivity tensor mapping of the human brain using diffusion tensor MRI

Knowledge of the electrical conductivity properties of excitable tissues is essential for relating the electromagnetic fields generated by the tissue to the underlying electrophysiological currents. Efforts to characterize these endogenous currents from measurements of the associated electromagnetic fields would significantly benefit from the ability to measure the electrical conductivity properties of the tissue noninvasively. Here, using an effective medium approach, we show how the electrical conductivity tensor of tissue can be quantitatively inferred from the water self-diffusion tensor as measured by diffusion tensor magnetic resonance imaging. The effective medium model indicates a strong linear relationship between the conductivity and diffusion tensor eigenvalues (respectively, σ and d) in agreement with theoretical bounds and experimental measurements presented here (σ/d ≈ 0.844 ± 0.0545 S⋅s/mm3, r2 = 0.945). The extension to other biological transport phenomena is also discussed.

Monte Carlo simulation studies of EEG and MEG localization accuracy

Both electroencephalography (EEG) and magnetoencephalography (MEG) are currently used to localize brain activity. The accuracy of source localization depends on numerous factors, including the specific inverse approach and source model, fundamental differences in EEG and MEG data, and the accuracy of the volume conductor model of the head (i.e., the forward model). Using Monte Carlo simulations, this study removes the effect of forward model errors and theoretically compares the use of EEG alone, MEG alone, and combined EEG/MEG data sets for source localization. Here, we use a linear estimation inverse approach with a distributed source model and a realistic forward head model. We evaluated its accuracy using the crosstalk and point spread metrics. The crosstalk metric for a specified location on the cortex describes the amount of activity incorrectly localized onto that location from other locations. The point spread metric provides the complementary measure: for that same location, the point spread describes the mis‐localization of activity from that specified location to other locations in the brain. We also propose and examine the utility of a “noise sensitivity normalized” inverse operator. Given our particular forward and inverse models, our results show that 1) surprisingly, EEG localization is more accurate than MEG localization for the same number of sensors averaged over many source locations and orientations; 2) as expected, combining EEG with MEG produces the best accuracy for the same total number of sensors; 3) the noise sensitivity normalized inverse operator improves the spatial resolution relative to the standard linear estimation operator; and 4) use of an a priori fMRI constraint universally reduces both crosstalk and point spread. Hum. Brain Mapping 16:47–62, 2002.

Task-modulated “what” and “where” pathways in human auditory cortex

Human neuroimaging studies suggest that localization and identification of relevant auditory objects are accomplished via parallel parietal-to-lateral-prefrontal “where” and anterior-temporal-to-inferior-frontal “what” pathways, respectively. Using combined hemodynamic (functional MRI) and electromagnetic (magnetoencephalography) measurements, we investigated whether such dual pathways exist already in the human nonprimary auditory cortex, as suggested by animal models, and whether selective attention facilitates sound localization and identification by modulating these pathways in a feature-specific fashion. We found a double dissociation in response adaptation to sound pairs with phonetic vs. spatial sound changes, demonstrating that the human nonprimary auditory cortex indeed processes speech-sound identity and location in parallel anterior “what” (in anterolateral Heschl’s gyrus, anterior superior temporal gyrus, and posterior planum polare) and posterior “where” (in planum temporale and posterior superior temporal gyrus) pathways as early as ≈70–150 ms from stimulus onset. Our data further show that the “where” pathway is activated ≈30 ms earlier than the “what” pathway, possibly enabling the brain to use top-down spatial information in auditory object perception. Notably, selectively attending to phonetic content modulated response adaptation in the “what” pathway, whereas attending to sound location produced analogous effects in the “where” pathway. This finding suggests that selective-attention effects are feature-specific in the human nonprimary auditory cortex and that they arise from enhanced tuning of receptive fields of task-relevant neuronal populations.

Parallel imaging reconstruction using automatic regularization.

Increased spatiotemporal resolution in MRI can be achieved by the use of parallel acquisition strategies, which simultaneously sample reduced k‐space data using the information from multiple receivers to reconstruct full‐FOV images. The price for the increased spatiotemporal resolution in parallel MRI is the degradation of the signal‐to‐noise ratio (SNR) in the final reconstructed images. Part of the SNR reduction results when the spatially correlated nature of the information from the multiple receivers destabilizes the matrix inversion used in the reconstruction of the full‐FOV image. In this work, a reconstruction algorithm based on Tikhonov regularization is presented that reduces the SNR loss due to geometric correlations in the spatial information from the array coil elements. Reference scans are utilized as a priori information about the final reconstructed image to provide regularized estimates for the reconstruction using the L‐curve technique. This automatic regularization method reduces the average g‐factors in phantom images from a two‐channel array from 1.47 to 0.80 in twofold sensitivity encoding (SENSE) acceleration. In vivo anatomical images from an eight‐channel system show an averaged g‐factor reduction of 1.22 to 0.84 in 2.67‐fold acceleration. Magn Reson Med 51:559–567, 2004.

Mapping function in the human brain with magnetoencephalography, anatomical magnetic resonance imaging, and functional magnetic resonance imaging.

Integrated analyses of human anatomical and functional measurements offer a powerful paradigm for human brain mapping. Magnetoencephalography (MEG) and EEG provide excellent temporal resolution of neural population dynamics as well as capabilities for source localization. Anatomical magnetic resonance imaging (MRI) provides excellent spatial resolution of head and brain anatomy, whereas functional MRI (fMRI) techniques provide an alternative measure of neural activation based on associated hemodynamic changes. These methodologies constrain and complement each other and can thereby improve our interpretation of functional neural organization. We have developed a number of computational tools and techniques for the visualization, comparison, and integrated analysis of multiple neuroimaging techniques. Construction of geometric anatomical models from volumetric MRI data allows improved models of the head volume conductor and can provide powerful constraints for neural electromagnetic source modeling. These approaches, coupled to enhanced algorithmic strategies for the inverse problem, can significantly enhance the accuracy of source-localization procedures. We have begun to apply these techniques for studies of the functional organization of the human visual system. Such studies have demonstrated multiple, functionally distinct visual areas that can be resolved on the basis of their locations, temporal dynamics, and differential sensitivity to stimulus parameters. Our studies have also produced evidence of internal retinotopic organization in both striate and extrastriate visual areas but have disclosed organizational departures from classical models. Comparative studies of MEG and fMRI suggest a reasonable but imperfect correlation between electrophysiological and hemodynamic responses. We have demonstrated a method for the integrated analysis of fMRI and MEG, and we outline strategies for improvement of these methods. By combining multiple measurement techniques, we can exploit the complementary strengths and transcend the limitations of the individual neuro-imaging methods.

Motion and Ballistocardiogram Artifact Removal for Interleaved Recording of EEG and EPs during MRI

Artifacts generated by motion (e.g., ballistocardiac) of the head inside a high magnetic field corrupt recordings of EEG and EPs. This paper introduces a method for motion artifact cancellation. This method is based on adaptive filtering and takes advantage of piezoelectric motion sensor information to estimate the motion artifact noise. This filter estimates the mapping between motion sensor and EEG space, subtracting the motion-related noise from the raw EEG signal. Due to possible subject motion and changes in electrode impedance, a time-varying mapping of the motion versus EEG is required. We show that this filter is capable of removing both ballistocardiogram and gross motion artifacts, restoring EEG alpha waves (8-13 Hz), and visual evoked potentials (VEPs). This adaptive filter outperforms the simple band-pass filter for alpha detection because it is also capable of reducing noise within the frequency band of interest. In addition, this filter also removes the transient responses normally visible in the EEG window after echo planar image acquisition, observed during interleaved EEG/fMRI recordings. Our adaptive filter approach can be implemented in real-time to allow for continuous monitoring of EEG and fMRI during clinical and cognitive studies.

The advantage of combining MEG and EEG: Comparison to fMRI in focally stimulated visual cortex

To exploit the high (millisecond) temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG) for measuring neuronal dynamics within well-defined brain regions, it is important to quantitatively assess their localizing ability. Previous modeling studies and empirical data suggest that a combination of MEG and EEG signals should yield the most accurate localization, due to their complementary sensitivities. However, these two modalities have rarely been explicitly combined for source estimation in studies of recorded brain activity, and a quantitative empirical assessment of their abilities, combined and separate, is currently lacking. Here we studied early visual responses to focal Gabor patches flashed during subject fixation. MEG and EEG data were collected simultaneously and were compared with the functional MRI (fMRI) localization produced by identical stimuli in the same subjects. This allowed direct evaluation of the localization accuracy of separate and combined MEG/EEG inverse solutions. We found that the localization accuracy of the combined MEG+EEG solution was consistently better than that of either modality alone, using three different source estimation approaches. Further analysis suggests that this improved localization is due to the different properties of the two imaging modalities rather than simply due to increased total channel number. Thus, combining MEG and EEG data is important for high-resolution spatiotemporal studies of the human brain.