Heritability of nested hierarchical structural brain network

Sex Differences of Cerebellum and Cerebrum: Evidence from Graph Convolutional Network

This work aims to exploit a novel graph neural network to predict the sex of the brain topological network, and to find the sex differences in the cerebrum and cerebellum. A two-branch multi-scale graph convolutional network (TMGCN) is designed to analyze the sex differences of the brain. Two complementary templates are used to construct cerebrum and cerebellum networks, respectively, followed by a two-branch sub-network with multi-scale filters and a trainable weighted fusion strategy for the final prediction. Finally, a trainable graph topk-pooling layer is utilized in our model to visualize key brain regions relevant to the prediction. The proposed TMGCN achieves a prediction accuracy of 84.48%. In the cerebellum, the bilateral Crus I-II, lobule VI and VIIb, and the posterior vermis (VI-X) are discriminative for this task. As for the cerebrum, the discriminative brain regions consist of the bilateral inferior temporal gyrus, the bilateral fusiform gyrus, the bilateral parahippocampal gyrus, the bilateral cingulate gyrus, the bilateral medial ventral occipital cortex, the bilateral lateral occipital cortex, the bilateral amygdala, and the bilateral hippocampus. This study tackles the sex prediction problem from a more comprehensive view, and may provide the resting-state fMRI evidence for further study of sex differences in the cerebellum and cerebrum.

Wisconsin Twin Project Overview: Temperament and Affective Neuroscience

The Wisconsin Twin Project encompasses nearly 30 years of longitudinal research that spans infancy to early adulthood. The twin sample was recruited from statewide birth records for birth cohorts 1989-2004. We summarize early recruitment, assessment, retention and recently completed twin neuroimaging studies. In addition to the focal twins, longitudinal data were also collected from two parents and nontwin siblings. Our adolescent and young adult neuroimaging sample (N = 600) completed several previous behavioral and environmental assessments, beginning shortly after birth. The extensive phenotyping is meant to support a range of empirical investigations with potentially differing theoretical perspectives.

Longitudinal Research at the Interface of Affective Neuroscience, Developmental Psychopathology, Health and Behavioral Genetics: Findings from the Wisconsin Twin Project

The Wisconsin Twin Project comprises multiple longitudinal studies that span infancy to early adulthood. We summarize recent papers that show how twin designs with deep phenotyping, including biological measures, can inform questions about phenotypic structure, etiology, comorbidity, heterogeneity, and gene-environment interplay of temperamental constructs and mental and physical health conditions of children and adolescents. The general framework for investigations begins with rich characterization of early temperament and follows with study of experiences and exposures across childhood and adolescence. Many studies incorporate neuroimaging and hormone assays.

Exact topological inference of the resting-state brain networks in twins

A cycle in a brain network is a subset of a connected component with redundant additional connections. If there are many cycles in a connected component, the connected component is more densely connected. Whereas the number of connected components represents the integration of the brain network, the number of cycles represents how strong the integration is. However, it is unclear how to perform statistical inference on the number of cycles in the brain network. In this study, we present a new statistical inference framework for determining the significance of the number of cycles through the Kolmogorov-Smirnov (KS) distance, which was recently introduced to measure the similarity between networks across different filtration values by using the zeroth Betti number. In this paper, we show how to extend the method to the first Betti number, which measures the number of cycles. The performance analysis was conducted using the random network simulations with ground truths. By using a twin imaging study, which provides biological ground truth, the methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the resting-state functional connectivity in 217 twins obtained from the Human Connectome Project. The MATLAB codes as well as the connectivity matrices used in generating results are provided at http://www.stat.wisc.edu/∼mchung/TDA.

In this paper, we propose a new topological distance based on the Kolmogorov-Smirnov (KS) distance that is adapted for brain networks, and compare them against other topological network distances including the Gromov-Hausdorff (GH) distances. KS-distance is recently introduced to measure the similarity between networks across different filtration values by using the zeroth Betti number, which measures the number of connected components. In this paper, we show how to extend the method to the first Betti number, which measures the number of cycles. The performance analysis was conducted using random network simulations with ground truths. Using a twin imaging study, which provides biological ground truth (of network differences), we demonstrate that the KS distances on the zeroth and first Betti numbers have the ability to determine heritability.