Experience-dependent alterations in the human brain's white-matter microstructure occur in early adulthood, but it is unknown whether such plasticity extends throughout life. We used cognitive training, diffusion-tensor imaging (DTI), and structural MRI to investigate plasticity of the white-matter tracts that connect the left and right hemisphere of the frontal lobes. Over a period of about 180 days, 20 younger adults and 12 older adults trained for a total of one hundred and one 1-h sessions on a set of three working memory, three episodic memory, and six perceptual speed tasks. Control groups were assessed at pre- and post-test. Training affected several DTI metrics and increased the area of the anterior part of the corpus callosum. These alterations were of similar magnitude in younger and older adults. The findings indicate that experience-dependent plasticity of white-matter microstructure extends into old age and that disruptions of structural interhemispheric connectivity in old age, which are pronounced in aging, are modifiable by experience and amenable to treatment.

In echo-planar-based diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI), the evaluation of diffusion parameters such as apparent diffusion coefficients and anisotropy indices is affected by image distortions that arise from residual eddy currents produced by the diffusion-sensitizing gradients. Correction methods that coregister diffusion-weighted and non-diffusion-weighted images suffer from the different contrast properties inherent in these image types. Here, a postprocessing correction scheme is introduced that makes use of the inverse characteristics of distortions generated by gradients with reversed polarity. In this approach, only diffusion-weighted images with identical contrast are included for correction. That is, non-diffusion-weighted images are not needed as a reference for registration. Furthermore, the acquisition of an additional dataset with moderate diffusion-weighting as suggested In diffusion-weighted echo-planar imaging (EPI), eddycurrent-related distortions add to the susceptibility-induced distortions that are generally seen on images measured by EPI. The magnitude and shape of these particular artifacts change with the chosen direction and strength of the applied diffusion-sensitizing gradients. Both diffusionweighted and non-diffusion-weighted images are necessary for the subsequent calculation of diffusion parameters such as apparent diffusion coefficients (ADCs) or anisotropy indices. Thus, significant miscalculations of maps depicting such parameters would occur if the acquired underlying images are warped against each other. Large errors are especially prominent in regions with high local contrast. While computational methods based on MR diffusion imaging, such as fiber tracking algorithms (1,2) or the mathematical modeling of high angular resolved diffusion data (3,4), become more and more sophisticated, attention must be paid to the reliability of the input data.Hence, an optimal distortion correction scheme for diffusion-weighted images still remains a goal.The high impact of eddy currents on diffusion-weighted imaging (DWI) is caused by strong diffusion-sensitizing magnetic field gradients flanked by short ramp times. Because in EPI the bandwidth in the phase-encoding direction is inevitably low, this direction is primarily prone to substantial eddy-current-generated distortions. Until now, several methods have been introduced to mitigate the problems caused by eddy-current-related distortions. Preemphasis settings may facilitate the generation of stabilized and, therefore, eddy-current-neutralizing gradient waveforms (5). Unfortunately, the application of a preemphasis technique does not fully eliminate eddy-currentrelated distortions. For this reason, other methods have been proposed in recent years that either reduce the eddy currents by modifications of the pulse sequence design or correct for the distortions during data postprocessing. Procedures using phantom scans as a reference have been suggested as well (6). Eddy-current-minimizing modifications of the...

LCModel and AMARES, two widely used quantitation tools for magnetic resonance spectroscopy (MRS) data, were employed to analyze simulated spectra similar to those typically obtained at short echo times (TEs) in the human brain at 1.5 T. The study focused mainly on the influence of signal-to-noise ratios (SNRs) and different linewidths on the accuracy and precision of the quantification results, and their effectiveness in accounting for the broad signal contribution of macromolecules and lipids (often called the baseline in in vivo MRS). When applied in their standard configuration (i.e., fitting a spline as a baseline for LCModel, and weighting the first data points for AMARES), both methods performed comparably but with their own characteristics. LCModel and AMARES quantitation benefited considerably from the incorporation of baseline information into the prior knowledge. However, the more accurate quantitation of the sum of glutamate and glutamine (Glx) favored the use of LCModel. Metabolite-to-creatine ratios estimated by LCModel with extended prior knowledge are more accurate than absolute concentrations, and are nearly independent of SNR and line broadening. In clinical magnetic resonance spectroscopy (MRS) of the brain, short echo times (TEs) are employed to optimize the signal-to-noise ratio (SNR), reduce signal attenuation due to transverse relaxation and scalar coupling, and enable the quantification of more than the three dominant singlet resonances (i.e., N-acetylaspartate (NAA), creatine (Cr), and choline (Cho)). Generally, it is difficult to quantify short-TE spectra because of the overlapping metabolite signals and the contribution of macromolecule and lipid components. In addition to software of MR tomographs and various in-house developments at research sites (1-3), two sophisticated and well documented software packages are widely used: LCModel (4) and Magnetic Resonance User Interface (MRUI) (5). These software packages are used worldwide by many groups not only because of their availability and performance, but also because they provide results with a broader basis of comparability. The commercially available software package LCModel (6) fits spectra in the frequency domain using a basis set of spectra of in vitro metabolite solutions acquired under conditions identical to those under which in vivo data are acquired. AMARES (7), which is part of the MRUI package (5), has the advantage of being free of charge to nonprofit organizations. This advanced quantitation toolbox analyzes spectra in the time domain utilizing a priori information that can be introduced flexibly. LCModel employs a "black box" approach, and thus requires less user interaction than AMARES.For application purposes, however, it is important to determine how quantitation depends on linewidth and SNR, and how the two methods handle the broad macromolecular and lipid signal contributions. Of course, the best way to account for macromolecular signal contribution is to acquire the macromolecular spectrum by inversion recovery (8) ...

Age-related degenerations in brain structure are associated with balance disturbances and cognitive impairment. However, neuroplasticity is known to be preserved throughout lifespan and physical training studies with seniors could reveal volume increases in the hippocampus (HC), a region crucial for memory consolidation, learning and navigation in space, which were related to improvements in aerobic fitness. Moreover, a positive correlation between left HC volume and balance performance was observed. Dancing seems a promising intervention for both improving balance and brain structure in the elderly. It combines aerobic fitness, sensorimotor skills and cognitive demands while at the same time the risk of injuries is low. Hence, the present investigation compared the effects of an 18-month dancing intervention and traditional health fitness training on volumes of hippocampal subfields and balance abilities. Before and after intervention, balance was evaluated using the Sensory Organization Test and HC volumes were derived from magnetic resonance images (3T, MP-RAGE). Fourteen members of the dance (67.21 ± 3.78 years, seven females), and 12 members of the fitness group (68.67 ± 2.57 years, five females) completed the whole study. Both groups revealed hippocampal volume increases mainly in the left HC (CA1, CA2, subiculum). The dancers showed additional increases in the left dentate gyrus and the right subiculum. Moreover, only the dancers achieved a significant increase in the balance composite score. Hence, dancing constitutes a promising candidate in counteracting the age-related decline in physical and mental abilities.

Background: Widespread cortical atrophy in Amyotrophic Lateral Sclerosis (ALS) has been described in neuropathological studies. The presence of cortical atrophy in conventional and scientific neuroimaging has been a matter of debate. In studies using computertomography, positron emission tomography, proton magnetic resonance spectroscopy and conventional T2-weighted and proton-weighted images, results have been variable. Recent morphometric studies by magnetic resonance imaging have produced conflicting results regarding the extent of grey and white matter involvement in ALS patients.