What to expect from MRI in the investigationof the central nervous system?

Structure of WM bundles constituting the working memory system in early multiple sclerosis: a quantitative DTI tractography study

Working memory impairment is frequently observed in patients with early multiple sclerosis (MS). MRI and functional MRI studies have shown that working memory impairment is mostly due to diffuse white matter (WM) damage affecting the connectivity between distant cortical areas. However, working memory deficits in early MS patients can be either completely or partly masked by compensatory functional plasticity. It seems likely that concomitantly with the WM bundle injury resulting from pathological processes, the functional plasticity present in early MS patients may be accompanied by reactive structural WM plasticity. This structural plasticity may effectively compensate for connectivity disturbances and/or contribute to functional brain reorganization. The diffusion characteristics of WM bundles involved in working memory were assessed here by performing quantitative diffusion tensor imaging (DTI) tractography on 24 patients with early relapsing-remitting MS and 15 healthy control subjects. The DTI tractography findings showed that WM connections constituting the executive system of working memory were structurally impaired (the fractional anisotropy was lower than normal and the mean diffusivity, higher than normal). A significantly larger number of connections between the left and right thalami was concurrently observed in the MS patients than in the control subjects, which suggests that the WM is endowed with reactive structural plasticity.

Regional cerebral blood volume (rCBV) and trasversal relaxation time (T2) mapping of the rat limbic system during pre-puberal and adult age

We analyzed modifications in transversal relaxation time (T2) and regional cerebral blood volume (rCBV) in two areas of the limbic system, i.e., olfactory bulb (OB) and amygdala (AMY), in pre-puberty and post-puberty female rats. The aim of this work was to extend the knowledge about physiological modifications of these MRI parameters at different developmental phases. No significant difference was observed in T2 values of the OB between the two groups (pre-puberty: T2 = 86.92 +/- 8.57 ms, post-puberty: T2 = 88.11 +/- 13.06 ms; mean +/- S.D.). On the contrary T2 values of the AMY were significantly different (P = 0.0001) between the two groups (pre-puberty 76.08 +/- 3.2, post-puberty 81.77 +/- 11.77 ms). rCBV values of OB were significantly different (P = 0.0025) between pre-puberty (0.38 +/- 0.12 a.u.) and post-puberty female rats (0.15 +/- 0.09 a.u.). A significant decrease in rCBV (P = 5.1 x 10(-13)) between pre-puberty and post-puberty females (pre-puberty: 0.36 +/- 0.12, post-puberty: 0.07 +/- 0.05 a.u.) was also observed in the AMY. These findings suggest that in the limbic system, microvascular plasticity parallels neuronal maturation and indicate the importance of an appropriate baseline study in experiments dealing with the limbic system performed at different time-points.

Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method

The Gegenbauer image reconstruction method, previously shown to improve the quality of magnetic resonance images, is utilized in this study as a segmentation preprocessing step. It is demonstrated that, for all simulated and real magnetic resonance images used in this study, the Gegenbauer reconstruction method improves the accuracy of segmentation. Although it is more desirable to use the k-space data for the Gegenbauer reconstruction method, only information acquired from MR images is necessary for the reconstruction, making the procedure completely self-contained and viable for all human brain segmentation algorithms.

Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results

We present a description, biological results and a reliability analysis for the method of diffusion tensor tracking (DTT) of white matter fiber pathways. In DTT, diffusion-tensor MRI (DT-MRI) data are collected and processed to visualize the line trajectories of fiber bundles within white matter (WM) pathways of living humans. A detailed description of the data acquisition is given. Technical aspects and experimental results are illustrated for the geniculo-calcarine tract with broad projections to visual cortex, occipital and parietal U-fibers, and the temporo-calcarine ventral pathway. To better understand sources of error and to optimize the method, accuracy and precision were analyzed by computer simulations. In the simulations, noisy DT-MRI data were computed that would be obtained for a WM pathway having a helical trajectory passing through gray matter. The error vector between the real and ideal track was computed, and random errors accumulated with the square root of track length consistent with a random-walk process. Random error was most dependent on signal-to-noise ratio, followed by number of averages, pathway anisotropy and voxel size, in decreasing order. Systematic error only occurred for a few conditions, and was most dependent on the stepping algorithm, anisotropy of the surrounding tissue, and non-equal voxel dimensions. Both random and systematic errors were typically below the voxel dimension. Other effects such as track rebound and track recovery also depended on experimental conditions. The methods, biological results and error analysis herein may improve the understanding and optimization of DTT for use in various applications in neuroscience and medicine.

Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images

Current semiautomated magnetic resonance (MR)-based brain segmentation and volume measurement methods are complex and not sufficiently accurate for certain applications. We have developed a simpler, more accurate automated algorithm for whole-brain segmentation and volume measurement in T(1)-weighted, three-dimensional MR images. This histogram-based brain segmentation (HBRS) algorithm is based on histograms and simple morphological operations. The algorithm's three steps are foreground/background thresholding, disconnection of brain from skull, and removal of residue fragments (sinus, cerebrospinal fluid, dura, and marrow). Brain volume was measured by counting the number of brain voxels. Accuracy was determined by applying HBRS to both simulated and real MR data. Comparing the brain volume rendered by HBRS with the volume on which the simulation is based, the average error was 1.38%. By applying HBRS to 20 normal MR data sets downloaded from the Internet Brain Segmentation Repository and comparing them with expert segmented data, the average Jaccard similarity was 0.963 and the kappa index was 0.981. The reproducibility of brain volume measurements was assessed by comparing data from two sessions (four total data sets) with human volunteers. Intrasession variability of brain volumes for sessions 1 and 2 was 0.55 +/- 0.56 and 0.74 +/- 0.56%, respectively; the mean difference between the two sessions was 0.60 +/- 0.46%. These results show that the HBRS algorithm is a simple, fast, and accurate method to determine brain volume with high reproducibility. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving MRI data are needed.