In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner.

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-Gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (Kiso), variance due to diffusion anisotropy (Kaniso), and microscopic kurtosis (µK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, µK is typically ignored in diffusion MRI signal modelling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible µK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of µK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that µK significantly contributes to total diffusional kurtosis both in grey and white matter tissue but, as expected, not in the ventricles. The first µK maps of the human brain are presented, revealing that the spatial distribution of µK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring µK and assuming the multiple Gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.

White matter connectivity between occipital and temporal regions involved in face and voice processing in hearing and early deaf individuals.

Neuroplasticity following sensory deprivation has long inspired neuroscience research in the quest of understanding how sensory experience and genetics interact in developing the brain functional and structural architecture. Many studies have shown that sensory deprivation can lead to cross-modal functional recruitment of sensory deprived cortices. Little is known however about how structural reorganization may support these functional changes. In this study, we examined early deaf, hearing signer and hearing non-signer individuals using diffusion MRI to evaluate the potential structural connectivity linked to the functional recruitment of the temporal voice area by face stimuli in deaf individuals. More specifically, we characterized the structural connectivity between occipital, fusiform and temporal regions typically supporting voice- and face-selective processing. Despite the extensive functional reorganization for face processing in the temporal cortex of the deaf, macroscopic properties of these connections did not differ across groups. However, both occipito- and fusiform-temporal connections showed significant microstructural changes between groups (fractional anisotropy reduction, radial diffusivity increase). We propose that the reorganization of temporal regions after early auditory deprivation builds on intrinsic and mainly preserved anatomical connectivity between functionally specific temporal and occipital regions.

The role of the default mode network in longitudinal functional brain reorganization of brain gliomas.

The study of patients after glioma resection offers a unique opportunity to investigate brain reorganization. It is currently unknown how the whole-brain connectomic profile evolves longitudinally after surgical resection of a glioma and how this may be associated with tumor characteristics and cognitive outcome. In this longitudinal study, we investigate the impact of tumor lateralization and grade on functional connectivity (FC) in highly connected networks, or hubs, and cognitive performance. Twenty-eight patients (17 high-grade, 11 low-grade gliomas) underwent longitudinal pre/post-surgery resting-state fMRI scans and neuropsychological assessments (73 total measures). FC matrices were constructed considering as functional hubs the default mode (DMN) and fronto-parietal networks. No-hubs included primary sensory functional networks and any other no-hubs nodes. Both tumor hemisphere and grade affected brain reorganization post-resection. In right-hemisphere tumor patients, regardless of grade and relative to left-hemisphere gliomas, FC increased longitudinally after the intervention, both in terms of FC within hubs (phubs = 0.0004) and FC between hubs and no-hubs (phubs-no-hubs = 0.005). Regardless of tumor side, only lower-grade gliomas showed longitudinal FC increases relative to high-grade tumors within a precise hub network, the DMN. The neurocognitive profile was longitudinally associated with spatial features of the connectome, mainly within the DMN. We provide evidence that clinical glioma features, such as lateralization and grade, affect post-surgical longitudinal functional reorganization and cognitive recovery. The data suggest a possible role of the DMN in supporting cognition, providing useful information for prognostic prediction and surgical planning.

Longitudinal Changes in Brain Diffusion MRI Indices during and after Proton Beam Therapy in a Child with Pilocytic Astrocytoma: A Case Report.

Proton beam therapy (PBT) is an effective pediatric brain tumor treatment. However, the resulting microstructural changes within and around irradiated tumors are unknown. We retrospectively applied diffusion tensor imaging (DTI) and free-water imaging (FWI) on diffusion-weighted magnetic resonance imaging (dMRI) data to monitor microstructural changes during the PBT and after 8 months in a pilocytic astrocytoma (PA) and normal-appearing white matter (NAWM). We evaluated the conventional MRI- and dMRI-derived indices from six MRI sessions (t0-t5) in a Caucasian child with a hypothalamic PA: at baseline (t0), during the PBT (t1-t4) and after 8 months (t5). The tumor voxels were classified as "solid" or "fluid" based on the FWI. While the tumor volume remained stable during the PBT, the dMRI analyses identified two different response patterns: (i) an increase in fluid content and diffusivity with anisotropy reductions in the solid voxels at t1, followed by (ii) smaller variations in fluid content but higher anisotropy in the solid voxels at t2-t4. At follow-up (t5), the tumor volume, fluid content, and diffusivity in the solid voxels increased. The NAWM showed dose-dependent microstructural changes. The use of the dMRI and FWI showed complex dynamic microstructural changes in the irradiated mass during the PBT and at follow-up, opening new avenues in our understanding of radiation-induced pathophysiologic mechanisms in tumors and the surrounding tissues.