ABSTRACT
Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) exhibits non-Euclidean topological structures, which have pathological foundations and serve as ideal objective data for intelligent diagnosis of major depressive disorder (MDD) patients. Additionally, the fully connected FC demonstrates uniform spatial structures. To learn and integrate information from these two structural forms for a more comprehensive identification of MDD patients, we propose a novel hierarchical learning structure called Multi-View Graph Neural Network (MV-GNN). In MV-GNN, the collaborative FC of subjects is filtered and reconstructed from topological view to obtain the reconstructed FC, incorporating various threshold values to calculate the topological attributes of brain regions. ROC analysis is performed on the average scores of these attributes for MDD and healthy control (HC) groups to determine an efficient threshold. Group differences analysis is conducted on the efficient topological attributes of brain regions, followed by their selection. These efficient attributes, along with the reconstructed FC, are combined to construct a graph view using self-attention graph pooling and graph convolutional neural networks, enabling efficient embedding. To extract efficient FC pattern difference information from spatial view, a dual leave-one-out cross-feature selection method is proposed. It selects and extracts relevant information from uniformly sized FC structures' high-dimensional spatial features, constructing a relationship view between brain regions. This approach incorporates both the whole graph topological view and spatial relationship view in a multi-layered structure, fusing them using gating mechanisms. By incorporating multiple views, it enhances the inference of whether subjects suffer from MDD and reveals differential information between MDD and HC groups across different perspectives. The proposed model structure is evaluated through leave-one-site cross-validation and achieves an average accuracy of 65.61% in identifying MDD patients at a single-center site, surpassing state-of-the-art methods in MDD recognition. The model provides valuable discriminatory information for objective diagnosis of MDD and serves as a reference for pathological foundations.
ABSTRACT
The MEGA-PRESS method is the most common method used to measure γ-aminobutyric acid (GABA) in the brain at 3T. It has been shown that the underestimation of the GABA signal due to B0 drift up to 1.22 Hz/min can be reduced by post-frequency alignment. In this study, we show that the underestimation of GABA can still occur even with post frequency alignment when the B0 drift is up to 3.93 Hz/min. The underestimation can be reduced by applying a frequency shift threshold. A total of 23 subjects were scanned twice to assess the short-term reproducibility, and 14 of them were scanned again after 2-8 weeks to evaluate the long-term reproducibility. A linear regression analysis of the quantified GABA versus the frequency shift showed a negative correlation (P < 0.01). Underestimation of the GABA signal was found. When a frequency shift threshold of 0.125 ppm (15.5 Hz or 1.79 Hz/min) was applied, the linear regression showed no statistically significant difference (P > 0.05). Therefore, a frequency shift threshold at 0.125 ppm (15.5 Hz) can be used to reduce underestimation during GABA quantification. For data with a B0 drift up to 3.93 Hz/min, the coefficients of variance of short-term and long-term reproducibility for the GABA quantification were less than 10% when the frequency threshold was applied.
