The dorsal striatum, an essential nucleus in subcortical areas, has a crucial role in controlling a variety of complex cognitive behaviors; however, few studies have been conducted in recent years to explore the functional subregions of the dorsal striatum that are significantly activated when performing multiple tasks. To explore the differences and connections between the functional subregions of the dorsal striatum that are significantly activated when performing different tasks, we propose a framework for functional division of the dorsal striatum based on a graph neural network model. First, time series information for each voxel in the dorsal striatum is extracted from acquired functional magnetic resonance imaging data and used to calculate the connection strength between voxels. Then, a graph is constructed using the voxels as nodes and the connection strengths between voxels as edges. Finally, the graph data are analyzed using the graph neural network model to functionally divide the dorsal striatum. The framework was used to divide functional subregions related to the four tasks including olfactory reward, "0-back" working memory, emotional picture stimulation, and capital investment decision-making. The results were further subjected to conjunction analysis to obtain 15 functional subregions in the dorsal striatum. The 15 different functional subregions divided based on the graph neural network model indicate that there is functional differentiation in the dorsal striatum when the brain performs different cognitive tasks. The spatial localization of the functional subregions contributes to a clear understanding of the differences and connections between functional subregions.

Background:The dorsal striatum, a nucleus in the basal ganglia, plays a key role in the execution of cognitive functions in the human brain. Recent studies have focused on how the dorsal striatum participates in a single cognitive function, whereas the specific roles of the caudate and putamen in performing multiple cognitive functions remain unclear. In this paper we conducted a meta-analysis of the relevant neuroimaging literature to understand the roles of subregions of the dorsal striatum in performing different functions.Methods:PubMed, Web of Science, and BrainMap Functional Database were searched to find original functional magnetic resonance imaging (fMRI) studies conducted on healthy adults under reward, memory, emotion, and decision-making tasks, and relevant screening criteria were formulated. Single task activation, contrast activation, and conjunction activation analyses were performed using the activation likelihood estimation (ALE) method for the coordinate-based meta-analysis to evaluate the differences and linkages.Results:In all, 112 studies were included in this meta-analysis. Analysis revealed that, of the 4 single activation tasks, reward, memory, and emotion tasks all activated the putamen more, whereas decision-making tasks activated the caudate body. Contrast analysis showed that the caudate body played an important role in the 2 cooperative activation tasks, but conjunction activation results found that more peaks appeared in the caudate head.Discussion:Different subregions of the caudate and putamen assume different roles in processing complex cognitive behaviors. Functional division of the dorsal striatum identified specific roles of 15 different subregions, reflecting differences and connections between the different subregions in performing different cognitive behaviors.

Brain image registration is a key part of brain image processing, so the research of brain image registration technology has important value. This paper proposes a brain MR image registration algorithm based on an improved convolutional neural network (CNN). Firstly, the improved CNN is used to extract the feature points in the brain image. Thereafter, the feature pre-matching is performed by finding the nearest neighbors in the feature space. Finally, the registration result of the brain image is obtained by combining the space transformation and the interpolation function. The extensive experiments show the improved CNN method obtains the better results compared with the classic SIFT algorithm and SURF algorithm. At the same time, compared with the CNN algorithm, the mutual information (MI), normalized cross-correlation coefficient (NCC) and normalized mutual information (NMI) of the improved CNN are improved by 10.55%, 0.62% and 1.84%, respectively, while mean square difference (MSD) decreasing by 0.65%. The results show that the algorithm based on the improved CNN achieves higher quality of registration.

BACKGROUND:Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy.METHODS:As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method.RESULTS:For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods.CONCLUSIONS:The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.