Spatial-temporal patterns of brain disconnectome in Alzheimer's disease.

Mounting evidences have shown that progression of white matter hyperintensities (WMHs) with vascular origin might cause cognitive dysfunction symptoms through their effects on brain networks. However, the vulnerability of specific neural connection related to WMHs in Alzheimer's disease (AD) still remains unclear. In this study, we established an atlas-guided computational framework based on brain disconnectome to assess the spatial-temporal patterns of WMH-related structural disconnectivity within a longitudinal investigation. Alzheimer's Disease Neuroimaging Initiative (ADNI) database was adopted with 91, 90 and 44 subjects including in cognitive normal aging, stable and progressive mild cognitive impairment (MCI), respectively. The parcel-wise disconnectome was computed by indirect mapping of individual WMHs onto population-averaged tractography atlas. By performing chi-square test, we discovered a spatial-temporal pattern of brain disconnectome along AD evolution. When applied such pattern as predictor, our models achieved highest mean accuracy of 0.82, mean sensitivity of 0.86, mean specificity of 0.82 and mean area under the receiver operating characteristic curve (AUC) of 0.91 for predicting conversion from MCI to dementia, which outperformed methods utilizing lesion volume as predictors. Our analysis suggests that brain WMH-related structural disconnectome contributes to AD evolution mainly through attacking connections between: (1) parahippocampal gyrus and superior frontal gyrus, orbital gyrus, and lateral occipital cortex; and (2) hippocampus and cingulate gyrus, which are also vulnerable to Aß and tau confirmed by other researches. All the results further indicate that a synergistic relationship exists between multiple contributors of AD as they attack similar brain connectivity at the prodromal stage of disease.

An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.

Recent studies have confirmed that white matter hyperintensities (WMHs) accumulated in strategic brain regions can predict cognitive impairments associated with Alzheimer's disease (AD). The knowledge of white matter anatomy facilitates lesion-symptom mapping associated with cognition, and provides important spatial information for lesion segmentation algorithms. However, deep learning-based methods in the white matter hyperintensity (WMH) segmentation realm do not take full advantage of anatomical knowledge in decision-making and lesion localization processes. In this paper, we proposed an anatomical knowledge-based MRI deep learning pipeline (AU-Net), handcrafted anatomical-based spatial features developed from brain atlas were integrated with a well-designed U-Net configuration to simultaneously segment and locate WMHs. Manually annotated data from WMH segmentation challenge were used for the evaluation. We then applied this pipeline to investigate the association between WMH burden and cognition within another publicly available database. The results showed that AU-Net significantly improved segmentation performance compared with methods that did not incorporate anatomical knowledge (p < 0.05), and achieved a 14-17% increase based on area under the curve (AUC) in the cohort with mild WMH burden. Different configurations for incorporating anatomical knowledge were evaluated, the proposed stage-wise AU-Net-two-step method achieved the best performance (Dice: 0.86, modified Hausdorff distance: 3.06 mm), which was comparable with the state-of-the-art method (Dice: 0.87, modified Hausdorff distance: 3.62 mm). We observed different WMH accumulation patterns associated with normal aging and cognitive impairments. Furthermore, the characteristics of individual WMH lesions located in strategic regions (frontal and parietal subcortical white matter, as well as corpus callosum) were significantly correlated with cognition after adjusting for total lesion volumes. Our findings suggest that AU-Net is a reliable method to segment and quantify brain WMHs in elderly cohorts, and is valuable in individual cognition evaluation.

A multi-family cluster of COVID-19 associated with asymptomatic and pre-symptomatic transmission in Jixi City, Heilongjiang, China, 2020.

We investigated a multi-family cluster of 22 cases in Jixi, where pre-symptomatic and asymptomatic transmission resulted in at least 41% of household infections of SARS-CoV-2. Our study illustrates the challenge of controlling COVID-19 due to the presence of asymptomatic and pre-symptomatic transmission even when extensive testing and contact tracing are conducted.

U-net combined with CRF and anatomical based spatial features to segment white matter hyperintensities.

White matter hyperintensities (WMH) are important biomarkers for cerebral small vessel disease and closely associated with other neurodegenerative process. In this paper, we proposed a fully automatic WMH segmentation method based on U-net architecture. CRF were combined with U-net to refine segmentation results. We used a new anatomical based spatial feature produced by brain tissue segmentation based on T1 image, along with intensities of T1 and T2-FLAIR images to train our neural network. We compared 8 forms of automated WMH segmentation methods, range from traditional statistical learnng methods to deep learning based methods, with different architecture and used different features. Results showed our proposed method achieved best performance in terms of most metrics, and the inclusion of anatomical based spatial features strongly increase the segmentation performance.

Perfusion Quantification using Arterial Spin Labeling Magnetic Resonance Imaging after Revascularization for Moyamoya Disease.

The hemodynamics in the brains of individuals with Moyamoya disease are complex and variable. Cerebral revascularization is an important treatment when hemodynamics are severely damaged. It's of great value to accurately quantify blood perfusion of different functional brain regions for better postoperative prognosis. In this study, we developed methods to segment territory of middle cerebral arteries (MCA) and its functional brain regions based on T1 and arterial spin labeling (ASL) imaging, absolute and normalized cerebral blood perfusion (CBF) were calculated for target regions-of-interest (ROI), spatial coefficient of variation was introduced to detect information of arterial transit time (ATT) contained in CBF images. After revascularization of Moyamoya disease, we detected perfusion improvement within MCA territory, while different alterations exist within different functional sub-regions. We also conformed that the spatial coefficient of variation of ASL CBF images can be used as an alternative ROI-based hemodynamic measurement to predict alterations of ATT. In summary, our methods show potential in postoperative evaluation of patients with Moyamoya disease.