Sungeun Kim

Tau is a hallmark pathology of Alzheimer's disease, and animal models have suggested that tau spreads from cell to cell through neuronal connections, facilitated by β-amyloid (Aβ). We test this hypothesis in humans using an epidemic spreading model (ESM) to simulate tau spread, and compare these simulations to observed patterns measured using tau-PET in 312 individuals along Alzheimer's disease continuum. Up to 70% of the variance in the overall spatial pattern of tau can be explained by our model. Surprisingly, the ESM predicts the spatial patterns of tau irrespective of whether brain Aβ is present, but regions with greater Aβ burden show greater tau than predicted by connectivity patterns, suggesting a role of Aβ in accelerating tau spread. Altogether, our results provide evidence in humans that tau spreads through neuronal communication pathways even in normal aging, and that this process is accelerated by the presence of brain Aβ.

We present the results of an H I aperture synthesis mosaic of the Large Magellanic Cloud (LMC), made by combining data from 1344 separate pointing centers using the Australia Telescope Compact Array (ATCA). The resolution of the mosaicked images is 10 (15 pc, using a distance to the LMC of 50 kpc). This mosaic, with a spatial resolution 15 times higher than that which had been previously obtained, emphasizes the turbulent and fractal structure of the ISM on the small scale, resulting from the dynamical feedback of the star formation processes with the ISM. The structure of the neutral atomic ISM in the LMC is dominated by H I filaments combined with numerous shells and holes. On the large scale, the H I disk appears to be remarkably symmetric and to have a well-organized and orderly, if somewhat complex, rotational field. The bulk of the H I resides in a disk 7.3 kpc in diameter. The mass of the disk component of the LMC is 2.5 × 109 M☉, and the upper limit to all mass within a radius of 4 kpc is ~3.5 × 109 M☉.

Alzheimer's disease (AD) is a progressive neurodegenerative condition marked by a decline in cognitive functions with no validated disease modifying treatment. It is critical for timely treatment to detect AD in its earlier stage before clinical manifestation. Mild cognitive impairment (MCI) is an intermediate stage between cognitively normal older adults and AD. To predict conversion from MCI to probable AD, we applied a deep learning approach, multimodal recurrent neural network. We developed an integrative framework that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers obtained from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The proposed framework integrated longitudinal multi-domain data. Our results showed that 1) our prediction model for MCI conversion to AD yielded up to 75% accuracy (area under the curve (AUC) = 0.83) when using only single modality of data separately; and 2) our prediction model achieved the best performance with 81% accuracy (AUC = 0.86) when incorporating longitudinal multi-domain data. A multi-modal deep learning approach has potential to identify persons at risk of developing AD who might benefit most from a clinical trial or as a stratification approach within clinical trials.