Hemispheric asymmetry is a fundamental organizing principle of the human brain. Answering the genetic effects of the asymmetry is a prerequisite for elucidating developmental mechanisms of brain asymmetries. Multi-modal magnetic resonance imaging (MRI) has provided an important tool for comprehensively interpreting human brain asymmetry and its genetic mechanism. By combining MRI data, individual differences in brain structural asymmetry have been investigated with quantitative genetic brain mapping using gene-heritability. Twins provide a useful natural model for studying the effects of genetics and environment on the brain. Studies based on MRI have found that the asymmetry of human brain structure has a genetic basis. From the perspective of quantitative genetic analysis, this article reviews recent findings on the genetic effects of asymmetry and genetic covariance between hemispheres from three aspects: the asymmetry of heritability, the heritability of asymmetry and the genetic correlation. At last, the article shows the limitations and future research directions in this field. The purpose of this systematic review is to quickly guide researchers to understand the origins and genetic mechanism of interhemispheric differences, and provide a genetic basis for further understanding and exploring individual differences in laterized cognitive behavior.
Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.
Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.