There have been conflicting results regarding the differences between men and women on the function and structure of the brain. To address this question, we propose a novel method to distinguish the effects of sex on brain structure and function based on identifying subgroups that maximize the between-sex differences. In a large sample of 19975 women and 17568 men, we demonstrate that our method can identify individuals at the extremities of the "maleness-femaleness" continuum and is able to quantify the maleness/femaleness of their features. These findings have widespread implications for studies assessing sex and its impact on the brain.
A growing number of studies suggest that detection of Alzheimer’s disease can be improved by using information derived from distinct neuroimaging modalities. However, so far it remains unresolved how these modalities can be combined within a deep learning model approach. In this study, we proposed a deep-neural-network model GapNet that can work with incomplete dataset including baseline and longitudinal MR, amyloid-PET, and FDG-PET data. We verified the effectiveness of GapNet by comparing it to the conventional Vanilla neural networks and specifically testing their performance in discriminating between healthy controls and individuals with amyloid changes, which is an important early pathological marker in Alzheimer’s Disease. Results showed that, compared to the Vanilla networks, GapNet achieved higher classification accuracy. In sum, our finding suggested that the GapNet model is a promising deep learning approach for detecting Alzheimer’s disease with multi-modal neuroimaging
Quantitative analysis of cell structures is essential for pharmaceutical drug screening and medical diagnostics. This work introduces a deep-learning-powered approach to extract quantitative biological information from brightfield microscopy images. Specifically, we train a conditional generative adversarial neural network (cGAN) to virtually stain lipid droplets, cytoplasm, and nuclei from brightfield images of human stem-cell-derived fat cells (adipocytes). Subsequently, we demonstrate that these virtually-stained images can be successfully employed to extract quantitative biologically relevant measures in a downstream cell-profiling analysis. To make this method readily available for future applications, we provide a Python software package that is available online for free access.
Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores – for example, the Dutch Lipid Score – are often employed as alternative, more accessible, albeit less accurate FH diagnostic tools. To overcome the limitations of these traditional methods and to obtain a more reliable approach to FH diagnosis we implement a “virtual” genetic test using machine-learning approaches.
The brain is a complex network that relies on the interaction between its various regions, known as the connectome. The human connectome undergoes complex changes with aging and shows differences in many functional network measures between men and women; however, the effects of aging and sex on the brain connectome are not well characterized. In this study, we assess functional connectivity changes in a large cohort of men and women between 45 and 79 years of age using conventional methods as well as a novel approach based on multilayer brain connectivity. Our findings provide a deeper insight into the sex differences that occur in functional connectivity over the course of aging. Moreover, they indicate that multilayer networks provide a natural way to integrate the information from positive and negative functional connections, providing important information on the effects of sex and age on network topology.
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