In vivo imaging of the human brain at 1.5 T with 0.6-mm isotropic resolution

A Single-Scan, Rapid Whole-Brain Protocol for Quantitative Water Content Mapping With Neurobiological Implications

Water concentration is tightly regulated in the healthy human brain and changes only slightly with age and gender in healthy subjects. Consequently, changes in water content are important for the characterization of disease. MRI can be used to measure changes in brain water content, but as these changes are usually in the low percentage range, highly accurate and precise methods are required for detection. The method proposed here is based on a long-TR (10 s) multiple-echo gradient-echo measurement with an acquisition time of 7:21 min. Using such a long TR ensures that there is no T₁ weighting, meaning that the image intensity at zero echo time is only proportional to the water content, the transmit field, and to the receive field. The receive and transmit corrections, which are increasingly large at higher field strengths and for highly segmented coil arrays, are multiplicative and can be approached heuristically using a bias field correction. The method was tested on 21 healthy volunteers at 3T field strength. Calibration using cerebral-spinal fluid values (~100% water content) resulted in mean values and standard deviations of the water content distribution in white matter and gray matter of 69.1% (1.7%) and 83.7% (1.2%), respectively. Measured distributions were coil-independent, as seen by using either a 12-channel receiver coil or a 32-channel receiver coil. In a test-retest investigation using 12 scans on one volunteer, the variation in the mean value of water content for different tissue types was ~0.3% and the mean voxel variability was ~1%. Robustness against reduced SNR was assessed by comparing results for 5 additional volunteers at 1.5T and 3T. Furthermore, water content distribution in gray matter is investigated and regional contrast reported for the first time. Clinical applicability is illustrated with data from one stroke patient and one brain tumor patient. It is anticipated that this fast, stable, easy-to-use, high-quality mapping method will facilitate routine quantitative MR imaging of water content.

Real diffusion-weighted MRI enabling true signal averaging and increased diffusion contrast

This project aims to characterize the impact of underlying noise distributions on diffusion-weighted imaging. The noise floor is a well-known problem for traditional magnitude-based diffusion-weighted MRI (dMRI) data, leading to biased diffusion model fits and inaccurate signal averaging. Here, we introduce a total-variation-based algorithm to eliminate shot-to-shot phase variations of complex-valued diffusion data with the intention to extract real-valued dMRI datasets. The obtained real-valued diffusion data are no longer superimposed by a noise floor but instead by a zero-mean Gaussian noise distribution, yielding dMRI data without signal bias. We acquired high-resolution dMRI data with strong diffusion weighting and, thus, low signal-to-noise ratio. Both the extracted real-valued and traditional magnitude data were compared regarding signal averaging, diffusion model fitting and accuracy in resolving crossing fibers. Our results clearly indicate that real-valued diffusion data enables idealized conditions for signal averaging. Furthermore, the proposed method enables unbiased use of widely employed linear least squares estimators for model fitting and demonstrates an increased sensitivity to detect secondary fiber directions with reduced angular error. The use of phase-corrected, real-valued data for dMRI will therefore help to clear the way for more detailed and accurate studies of white matter microstructure and structural connectivity on a fine scale.

Fear processing and social networking in the absence of a functional amygdala

BACKGROUND

The human amygdala plays a crucial role in processing social signals, such as face expressions, particularly fearful ones, and facilitates responses to them in face-sensitive cortical regions. This contributes to social competence and individual amygdala size correlates with that of social networks. While rare patients with focal bilateral amygdala lesion typically show impaired recognition of fearful faces, this deficit is variable, and an intriguing possibility is that other brain regions can compensate to support fear and social signal processing.

METHODS

To investigate the brain's functional compensation of selective bilateral amygdala damage, we performed a series of behavioral, psychophysiological, and functional magnetic resonance imaging experiments in two adult female monozygotic twins (patient 1 and patient 2) with equivalent, extensive bilateral amygdala pathology as a sequela of lipoid proteinosis due to Urbach-Wiethe disease.

RESULTS

Patient 1, but not patient 2, showed preserved recognition of fearful faces, intact modulation of acoustic startle responses by fear-eliciting scenes, and a normal-sized social network. Functional magnetic resonance imaging revealed that patient 1 showed potentiated responses to fearful faces in her left premotor cortex face area and bilaterally in the inferior parietal lobule.

CONCLUSIONS

The premotor cortex face area and inferior parietal lobule are both implicated in the cortical mirror-neuron system, which mediates learning of observed actions and may thereby promote both imitation and empathy. Taken together, our findings suggest that despite the pre-eminent role of the amygdala in processing social information, the cortical mirror-neuron system may sometimes adaptively compensate for its pathology.