NeuroImage
-
Curvilinear reformatting of three-dimensional (3D) MRI data of the cerebral cortex is a well-established tool which improves the display of the gyral structure, permits a precise localization of lesions, and helps to identify subtle abnormalities difficult to detect in planar slices due to the brain's complex convolutional pattern. However, the method is time consuming because it requires interactive manual delineation of the brain surface contour. Therefore, a novel technique for automatic curvilinear reformatting is presented. ⋯ Compared to cross-sectional images, curvilinear reformatting offers a markedly superior visualization of topographic relations between lesions and cortical structures, helps to detect subtle cortical malformations and to assess the spatial extent of lesions, thus allowing a better planning of neurosurgical procedures. Compared to alternative methods, it is largely based on freely available software and does not require observer-dependent manual input. In conclusion, we present a simple, easy-to-use and fully automated method for curvilinear reformatting of 3D MRI.
-
A powerful, non-invasive technique for estimating and visualizing white matter tracts in the human brain in vivo is white matter fiber tractography that uses magnetic resonance diffusion tensor imaging. The success of this method depends strongly on the capability of the applied tracking algorithm and the quality of the underlying data set. However, DTI-based fiber tractography still lacks standardized validation. ⋯ Second, fMRI-guided DTI fiber tracking was performed to generate DTI-based somatotopic maps, using a standard line propagation and an advanced fast marching algorithm. The results show that the fiber connections reconstructed by the advanced fast marching algorithm are in good agreement with known anatomy, and that the DTI-revealed somatotopy is similar to the fMRI somatotopy. Furthermore, the study illustrates that the combination of fMRI with DTI can supply additional information in order to choose reasonable seed regions for generating functionally relevant networks and to validate reconstructed fibers.
-
Our goal is to model the coupling between neuronal activity, cerebral metabolic rates of glucose and oxygen consumption, cerebral blood flow (CBF), electroencephalography (EEG) and blood oxygenation level-dependent (BOLD) responses. In order to accomplish this, two previous models are coupled: a metabolic/hemodynamic model (MHM) for a voxel, linking BOLD signals and neuronal activity, and a neural mass model describing the neuronal dynamics within a voxel and its interactions with voxels of the same area (short-range interactions) and other areas (long-range interactions). For coupling both models, we take as the input to the BOLD model, the number of active synapses within the voxel, that is, the average number of synapses that will receive an action potential within the time unit. ⋯ Results show that realistic evoked potentials (EP) at electrodes on the scalp surface and the corresponding BOLD signals for each voxel are produced by the model. In another simulation, the alpha rhythm was reproduced and reasonable similarities with experimental data were obtained when calculating correlations between BOLD signals and the alpha power curve. The origin of negative BOLD responses and the characteristics of EEG, PET and BOLD signals in Alzheimer's disease were also studied.