No abstract

Replicable risk factors for ALS include increasing age, family history and being male. The male: female ratio has been reported as being between 1 and 3. We tested the hypothesis that the sex ratio changes with age in a population register covering the south-east of England. The sex ratio before and after the age of 51 years was compared using a Z-test for proportions. Kendall's tau was used to assess the relationship between age group and sex ratio using incidence and prevalence data. Publicly available data from Italian and Irish population registers were compared with results. There was a significant difference in the proportion of females with ALS between those in the younger group (30.11%) and those in the older group (43.66%) (p = 0.013). The adjusted male: female ratio dropped from 2.5 in the younger group to 1.4 in the older group using prevalence data (Kendall's tau = -0.73, p = 0.039). Similar ratios were found in the Italian but not the Irish registry. We concluded that sex ratios in ALS may change with age. Over-representation of younger patients in clinic registers may explain the variation in sex ratios between studies. Menopause may also play a role.

Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained with our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on multiple standard face alignment datasets. Our method's estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.

Diffusion MRI fiber tracking provides a non-invasive method for mapping the trajectories of human brain connections, but its false connection problem has been a major challenge. This study introduces topology-informed pruning (TIP), a method that automatically identifies singular tracts and eliminates them to improve the tracking accuracy. The accuracy of the tractography with and without TIP was evaluated by a team of 6 neuroanatomists in a blinded setting to examine whether TIP could improve the accuracy. The results showed that TIP improved the tracking accuracy by 11.93% in the single-shell scheme and by 3.47% in the grid scheme. The improvement is significantly different from a random pruning (p value < 0.001). The diagnostic agreement between TIP and neuroanatomists was comparable to the agreement between neuroanatomists. The proposed TIP algorithm can be used to automatically clean-up noisy fibers in deterministic tractography, with a potential to confirm the existence of a fiber connection in basic neuroanatomical studies or clinical neurosurgical planning.

CN, cranial nerveDSI, diffusion spectrum imagingDTI, diffusion tensor imagingHCP, Human Connectome ProjectHDFT, high-definition fiber tractographyMLF, medial longitudinal fasciculusODF, orientation distribution functionROI, region of interest.