Medical imaging modalities, such as magnetic resonance (MR), histological images, and positron emission tomography (PET), enable study of anatomy and function in animals and humans. The technology to collect such data greatly exceeds tools to analyze it. This research seeks to address this issue by developing methods that automatically synthesize labeled electronic atlases tailored to individuals. The approach

This paper describes a method to infer the connectivity induced by white matter fibers in the living human brain. This method stems from magnetic resonance tensor imaging (DTI), a technique which gives access to fiber orientations. Given typical DTI spatial resolution, connectivity is addressed at the level of fascicles made up by a bunch of parallel fibers. We propose first an algorithm dedicated to fascicle tracking in a direction map inferred from diffusion data. This algorithm takes into account fan-shaped fascicle forks usual in actual white matter organization. Then, we propose a method of inferring a regularized direction map from diffusion data in order to improve the robustness of the tracking. The regularization stems from an analogy between white matter organization and spaghetti plates. Finally, we propose a study of the tracking behavior according to the weight given to the regularization and some examples of the tracking results with in vivo human brain data. 2001 Elsevier Science B.V. All rights reserved.

Introduction The ever expanding gamut of medical imaging techniques provides the clinician an increasingly multifaceted view of brain function and anatomy. The information provided by the various imaging modalities is often complementary (i.e. provides separate but useful information) and synergistic (i.e. the combination of information provides useful extra information). For example, X-ray computed tomography (CT) and magnetic resonance (MR) imaging exquisitely demonstrate brain anatomy but provide little functional information. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans display aspects of brain function and allow metabolic measurements but poorly delineate anatomy. Furthermore, CT and MR images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while soft-tissue structures are better differentiated by MR imaging. Clinical diagnosis and therapy planning and evaluatio

Written Chinese as logographic script differs notably from alphabets such as English in visual form, orthography, phonology, and semantics. Thus, research on the Chinese language is important to advance our understanding of the universality and particularity of the organization of language systems in the brain. In this study, we examine the neural systems associated with logographic reading using functional magnetic resonance imaging. Two experimental tasks were devised, one based on semantic decision and the other on homophone decision. Compared to the fixation baseline, peak activations resulting from semantic as well as homophony decisions were localized in the left middle frontal gyrus (BA 9). Left inferior frontal cortex also mediated Chinese processing. In addition, more right hemisphere cortical regions (i.e., BAs 47/45, 7, 40/39, and the right visual system) were involved in reading Chinese relative to reading English. This is attributed to the square shape of the logograph which requires an elaborated analysis of the spatial information and locations of various strokes comprising the logographic character. We suggest that the left middle frontal area (BA 9) coordinates and integrates the intensive visuospatial analysis demanded by logographs ’ square configuration and the semantic (or phonological) analysis required by the present experimental tasks. Our study has implicated brain regions common to both logographic and alphabetic languages as well as brain regions specialized in processing logographs. © 2001 Academic Press

Advanced imaging techniques have been widely used to study the anatomical structure and functional metabolism in medical and clinical applications. Images are acquired from a variety of scanners (CT/MR/PET/SPECT/Ultrasound), which provide physicians with complementary information to diagnose and detect specific regions of a patient. However, due to the different modalities and imaging orientations, these images rarely align spatially. They need to be registered for consistent and repeatable analyses. Therefore, image registration is a critical component of medical imaging applications. Since the brains of rodent animal mostly behave in the rigid manner, their alignments may be generally described by a rigid model without local deformation. Mutual information is an excellent strategy to measure the statistical dependence of image from mono-modality or multi-modalities. The registration system with rigid model was developed to combine with mutual information for functional magnetic resonance (fMRI) analysis, which has five components: (1) rigid body and affine transformation, (2) mutual information as the similarity measure, (3) partial volume interpolation, (4) multi-

Conventional three dimensional medical imaging devices have made possible the routine visualization of biological tissue acquired as a sequence of image volumes over time. Because biological tissue is rarely static, the accurate registration of features in two volumes from an image sequence is a common requirement for analysis of these data. This registration task can be difficult, since tissue can bend and stretch over time, and a motion description capturing the non-rigid deformation can be quite complex. One way to characterize the non-rigid deformation is by means of a vector field called a motion field, which describes the relative displacement of each voxel, and thus establishes a correspondence between any set of features in the two volumes. A field such as this can adequately describe any non-...

Abstract—Electrocorticography (ECoG) has been demonstrated to be an effective modality as a platform for brain–computer interfaces (BCIs). Through our experience with ten subjects, we further demonstrate evidence to support the power and flexibility of this signal for BCI usage. In a subset of four patients, closed-loop BCI experiments were attempted with the patient receiving online feedback that consisted of one-dimensional cursor movement controlled by ECoG features that had shown correlation with various real and imagined motor and speech tasks. All four achieved control, with final target accuracies between 73%–100%. We assess the methods for achieving control and the manner in which enhancing online control can be accomplished by rescreening during online tasks. Additionally, we assess the relevant issues of the current experimental paradigm in light of their clinical constraints. Index Terms—Brain–computer interface (BCI), brain–machine interface (BMI), neuroprosthetics. I.

We present a method to detect intensity changes in longitudinal volumetric MRI data from patients with multiple sclerosis (MS).

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