a b s t r a c tThe global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated postprocessing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.

The complex structural organization of the white matter of the brain can be depicted in vivo in great detail with advanced diffusion magnetic resonance (MR) imaging schemes. Diffusion MR imaging techniques are increasingly varied, from the simplest and most commonly used technique-the mapping of apparent diffusion coefficient values-to the more complex, such as diffusion tensor imaging, q-ball imaging, diffusion spectrum imaging, and tractography. The type of structural information obtained differs according to the technique used. To fully understand how diffusion MR imaging works, it is helpful to be familiar with the physical principles of water diffusion in the brain and the conceptual basis of each imaging technique. Knowledge of the technique-specific requirements with regard to hardware and acquisition time, as well as the advantages, limitations, and potential interpretation pitfalls of each technique, is especially useful. © RSNA, 2006Abbreviations: ADC ϭ apparent diffusion coefficient, SE ϭ spin echo, 6D ϭ six-dimensional, 3D ϭ three-dimensional

The purpose of this study was to determine the prognostic accuracy of perfusion computed tomography (CT), performed at the time of emergency room admission, in acute stroke patients. Accuracy was determined by comparison of perfusion CT with delayed magnetic resonance (MR) and by monitoring the evolution of each patient's clinical condition. Twentytwo acute stroke patients underwent perfusion CT covering four contiguous 10mm slices on admission, as well as delayed MR, performed after a median interval of 3 days after emergency room admission. Eight were treated with thrombolytic agents. Infarct size on the admission perfusion CT was compared with that on the delayed diffusion-weighted (DWI)-MR, chosen as the gold standard. Delayed magnetic resonance angiography and perfusion-weighted MR were used to detect recanalization. A potential recuperation ratio, defined as PRR ‫؍‬ penumbra size/(penumbra size ؉ infarct size) on the admission perfusion CT, was compared with the evolution in each patient's clinical condition, defined by the National Institutes of Health Stroke Scale (NIHSS). In the 8 cases with arterial recanalization, the size of the cerebral infarct on the delayed DWI-MR was larger than or equal to that of the infarct on the admission perfusion CT, but smaller than or equal to that of the ischemic lesion on the admission perfusion CT; and the observed improvement in the NIHSS correlated with the PRR (correlation coefficient ‫؍‬ 0.833). In the 14 cases with persistent arterial occlusion, infarct size on the delayed DWI-MR correlated with ischemic lesion size on the admission perfusion CT (r ‫؍‬ 0.958). In all 22 patients, the admission NIHSS correlated with the size of the ischemic area on the admission perfusion CT (r ‫؍‬ 0.627). Based on these findings, we conclude that perfusion CT allows the accurate prediction of the final infarct size and the evaluation of clinical prognosis for acute stroke patients at the time of emergency evaluation. It may also provide information about the extent of the penumbra. Perfusion CT could therefore be a valuable tool in the early management of acute stroke patients.

Our initial report suggests that high-resolution brain perfusion imaging is feasible with IVIM in the current clinical setting.

Evidence from psychophysical studies in normal and brain-damaged subjects suggests that auditory information relevant to recognition and localization are processed by distinct neuronal populations. We report here on anatomical segregation of these populations. Brain activation associated with performance in sound identification and localization was investigated in 18 normal subjects using fMRI. Three conditions were used: (i) comparison of spatial stimuli simulated with interaural time differences; (ii) identification of environmental sounds; and (iii) rest. Conditions (i) and (ii) required acknowledgment of predefined targets by pressing a button. After coregistering, images were normalized and smoothed. Activation patterns were analyzed using SPM99 for individual subjects and for the whole group. Sound recognition and localization activated, as compared to rest, inferior colliculus, medial geniculate body, Heschl gyrus, and parts of the temporal, parietal, and frontal convexity bilaterally. The activation pattern on the fronto-temporo-parietal convexity differed in the two conditions. Middle temporal gyrus and precuneus bilaterally and the posterior part of left inferior frontal gyrus were more activated by recognition than by localization. Lower part of inferior parietal lobule and posterior parts of middle and inferior frontal gyri were more activated, bilaterally, by localization than by recognition. Regions selectively activated by sound recognition, but not those selectively activated by localization, were significantly larger in women. Passive listening paradigm revealed segregated pathways on superior temporal gyrus and inferior parietal lobule. Thus, anatomically distinct networks are involved in sound recognition and sound localization.