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BackgroundSurvivors of pediatric posterior fossa brain tumors are susceptible to the adverse effects of treatment as they grow into adulthood. While the exact neurobiological mechanisms of these outcomes are not yet understood, the effects of treatment on white matter (WM) tracts in the brain can be visualized using diffusion tensor (DT) imaging. We investigated these WM microstructural differences using the statistical method tract-specific analysis (TSA). We applied TSA to the DT images of 25 children with a history of posterior fossa tumor (15 treated with surgery, 10 treated with surgery and chemotherapy) along with 21 healthy controls. Between these 3 groups, we examined differences in the most used DTI metric, fractional anisotropy (FA), in 11 major brain WM tracts.ResultsLower FA was found in the splenium of the corpus callosum (CC), the bilateral corticospinal tract (CST), the right inferior frontal occipital fasciculus (IFOF) and the left uncinate fasciculus (UF) in children with brain tumors as compared to healthy controls. Lower FA, an indicator of microstructural damage to WM, was observed in 4 of the 11 WM tracts examined in both groups of children with a history of posterior fossa tumor, with an additional tract unique to children who received surgery and chemotherapy (left UF).ConclusionsOur findings indicate that a history of tumor in the posterior fossa and surgical resection may have effects on the WM in other parts of the brain.

Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM)., Comment: 10 pages, 5 figures, 17th International Symposium on Medical Information Processing and Analysis

Diffusion weighted imaging (DWI) can reveal the orientation of the underlying fiber populations in the brain. High angular resolution diffusion imaging (HARDI) is increasingly used to better resolve the orientation and mixing of fibers. Here, we assessed the added value of multi-shell q-space sampling on the reconstruction of major fibers using mathematical frameworks from q-ball imaging (QBI) and generalized q-sampling imaging (GQI), as compared to diffusion tensor imaging (DTI). We scanned a healthy mouse brain using 7-Tesla 5-shell HARDI (b=1000, 3000, 4000, 8000, 12000 s/mm2), also known as hybrid diffusion imaging (HYDI). We found that QBI may provide greater reconstruction accuracy for major fibers, which improves with the addition of higher b-value shells, unlike GQI or DTI (as expected). Although QBI is a special case of GQI, the major fiber orientation in QBI was more closely related to the orientation in DTI, rather than GQI. HYDI can aid the clinical outcomes of research and especially – more advanced human and animal connectomics projects to map the brain’s neural pathways and networks.