We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.

We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability. D

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 Key words: diffusion; diffusion-weighted MRI (DWI); diffusion tensor imaging (DTI); white matter; tractographyTissues with regularly ordered microstructure, such as skeletal muscle, spine, tongue, heart, and cerebral white matter, exhibit anisotropic water diffusion due to the alignment of the diffusion compartments in the tissue (1-7). The direction of preferred diffusion, and hence the direction of preferred orientation in the tissue, can be resolved with a method called magnetic resonance (MR) diffusion tensor imaging (DTI) (7), which measures the apparent water self-diffusion tensor under the assumption of Gaussian diffusion. Based on the eigenstructure of the measured diffusion tensor, it is possible to infer the orientation of the diffusion compartments within the voxel so that, for example, the major eigenvector of the diffusion tensor parallels the mean fiber orientation (7), and the minor eigenvector parallels the normal to the mean plane of fiber dispersion (8).The tensor model is incapable, however, of resolving multiple fiber orientations within an individual voxel. This shortcoming of the tensor model stems from the fact that the tensor possesses only a single orientational maximum, i.e., the major eigenvalue of the diffusion tensor (9,10). At the millimeter-scale resolution typical of DTI, the volume of cerebral white matter containing such intravoxel orientational heterogeneity (IVOH) may be considerable given the widespread divergence and convergence of fascicles (11-13). The abundance of IVOH at the millimeter scale can be further appreciated by considering the ubiquity of oblate (pancake-shaped) diffusion tensors in DTI, a hypothesized indicator of IVOH (3,4,8).Given the obstacle that IVOH (particularly fiber crossing (14 -16)) poses to white matter tractography algorithms (14 -20), we sought to determine whether high angular resolution, high b-value diffusion gradient sampling could resolve such intravoxel heterogeneity (9). H...

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Previous research in non-human primates has shown that the superior longitudinal fascicle (SLF), a major intrahemispheric fiber tract, is actually composed of four separate components. In humans, only post-mortem investigations have been available to examine the trajectory of this tract. This study evaluates the hypothesis that the four subcomponents observed in non-human primates can also be found in the human brain using in vivo diffusion tensor magnetic resonance imaging (DT-MRI). The results of our study demonstrated that the four subdivisions could indeed be identified and segmented in humans. SLF I is located in the white matter of the superior parietal and superior frontal lobes and extends to the dorsal premotor and dorsolateral prefrontal regions. SLF II occupies the central core of the white matter above the insula. It extends from the angular gyrus to the caudal-lateral prefrontal regions. SLF III is situated in the white matter of the parietal and frontal opercula and extends from the supramarginal gyrus to the ventral premotor and prefrontal regions. The fourth subdivision of the SLF, the arcuate fascicle, stems from the caudal part of the superior temporal gyrus arches around the caudal end of the Sylvian fissure and extends to the lateral prefrontal cortex along with the SLF II fibers. Since DT-MRI allows the precise definition of only the stem portion of each fiber pathway, the origin and termination of the subdivisions of SLF are extrapolated from the available data in experimental material from non-human primates.

We investigated brain circuitry mediating cocaine-induced euphoria and craving using functional MRI (fMRI). During double-blind cocaine (0.6 mg/kg) and saline infusions in cocaine-dependent subjects, the entire brain was imaged for 5 min before and 13 min after infusion while subjects rated scales for rush, high, low, and craving. Cocaine induced focal signal increases in nucleus accumbens/subcallosal cortex (NAc/SCC), caudate, putamen, basal forebrain, thalamus, insula, hippocampus, parahippocampal gyrus, cingulate, lateral prefrontal and temporal cortices, parietal cortex, striate/extrastriate cortices, ventral tegmentum, and pons and produced signal decreases in amygdala, temporal pole, and medial frontal cortex. Saline produced few positive or negative activations, which were localized to lateral prefrontal cortex and temporo-occipital cortex. Subjects who underwent repeat studies showed good replication of the regional fMRI activation pattern following cocaine and saline infusions, with activations on saline retest that might reflect expectancy. Brain regions that exhibited early and short duration signal maxima showed a higher correlation with rush ratings. These included the ventral tegmentum, pons, basal forebrain, caudate, cingulate, and most regions of lateral prefrontal cortex. In contrast, regions that demonstrated early but sustained signal maxima were more correlated with craving than with rush ratings; such regions included the NAc/SCC, right parahippocampal gyrus, and some regions of lateral prefrontal cortex. Sustained negative signal change was noted in the amygdala, which correlated with craving ratings. Our data demonstrate the ability of fMRI to map dynamic patterns of brain activation following cocaine infusion in cocaine-dependent subjects and provide evidence of dynamically changing brain networks associated with cocaine-induced euphoria and cocaine-induced craving.

The etiology and consistency of findings on normal sexual dimorphisms of the adult human brain are unresolved. In this study, we present a comprehensive evaluation of normal sexual dimorphisms of cortical and subcortical brain regions, using in vivo magnetic resonance imaging, in a community sample of 48 normal adults. The men and women were similar in age, education, ethnicity, socioeconomic status, general intelligence and handedness. Forty-five brain regions were assessed based on T(1)-weighted three-dimensional images acquired from a 1.5 T magnet. Sexual dimorphisms of adult brain volumes were more evident in the cortex, with women having larger volumes, relative to cerebrum size, particularly in frontal and medial paralimbic cortices. Men had larger volumes, relative to cerebrum size, in frontomedial cortex, the amygdala and hypothalamus. A permutation test showed that, compared to other brain areas assessed in this study, there was greater sexual dimorphism among brain areas that are homologous with those identified in animal studies showing greater levels of sex steroid receptors during critical periods of brain development. These findings have implications for developmental studies that would directly test hypotheses about mechanisms relating sex steroid hormones to sexual dimorphisms in humans.

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