Purpose: The hippocampus is central to the pathophysiology of schizophrenia. Histology shows abnormalities in the dentate granule cell layer (DGCL), but its small size (~100 μm thickness) has precluded in vivo human studies. We used ultra high field magnetic resonance imaging (MRI) to compare DGCL morphology of schizophrenic patients to matched controls.
Method: Bilateral hippocampi of 16 schizophrenia patients (10 male) 40.7 ± 10.6 years old (mean ± standard deviation) were imaged at 7 Tesla MRI with heavily T₂*-weighted gradient-echo sequence at 232 μm in-plane resolution (0.08 μL image voxels). Fifteen matched controls (8 male, 35.6 ± 9.4 years old) and one ex vivo post mortem hippocampus (that also underwent histopathology) were scanned with same protocol. Three blinded neuroradiologists rated each DGCL on a qualitative scale of 1 to 6 (from "not discernible" to "easily visible, appearing dark gray or black") and mean left and right DGCL scores were compared using a non-parametric Mann-Whitney test.
Results: MRI identification of the DGCL was validated with histopathology. Mean right and left DGCL ratings in patients (3.2 ± 1.0 and 3.5 ± 1.2) were not statistically different from those of controls (3.9 ± 1.1 and 3.8 ± 0.8), but patients had a trend for lower right DGCL score (p = 0.07), which was significantly associated with patient diagnosis (p = 0.05). The optimal 48% sensitivity and 80% specificity for schizophrenia were achieved with a DGCL rating of ≤2.
Conclusion: Decreased contrast in the right DGCL in schizophrenia was predictive of schizophrenia diagnosis. Better utility of this metric as a schizophrenia biomarker may be achieved in future studies of patients with homogeneous disease subtypes and progression rates.
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http://dx.doi.org/10.1016/j.schres.2013.04.020 | DOI Listing |
Brief Bioinform
September 2024
School of Computer Science and Engineering, Sun Yat-sen University, Waihuan East Street, Guangzhou 510006, China.
In this paper, we propose DGCL, a dual-graph neural networks (GNNs)-based contrastive learning (CL) integrated with mixed molecular fingerprints (MFPs) for molecular property prediction. The DGCL-MFP method contains two stages. In the first pretraining stage, we utilize two different GNNs as encoders to construct CL, rather than using the method of generating enhanced graphs as before.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
December 2024
Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing brain network construction tools have some limitations, including dependency on empirical users, weak consistency in repeated experiments and time-consuming processes. In this work, a diffusion-based brain network pipeline, DGCL is designed for end-to-end construction of brain networks.
View Article and Find Full Text PDFDiagnostics (Basel)
October 2023
Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Diabetic retinopathy (DR) is a common complication of diabetes, which can lead to vision loss. Early diagnosis is crucial to prevent the progression of DR. In recent years, deep learning approaches have shown promising results in the development of an intelligent and efficient system for DR classification.
View Article and Find Full Text PDFBrief Bioinform
September 2023
College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 Hunan, China.
Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs.
View Article and Find Full Text PDFMicromachines (Basel)
July 2023
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China.
Rolling bearings are crucial mechanical components in the mechanical industry. Timely intervention and diagnosis of system faults are essential for reducing economic losses and ensuring product productivity. To further enhance the exploration of unlabeled time-series data and conduct a more comprehensive analysis of rolling bearing fault information, this paper proposes a fault diagnosis technique for rolling bearings based on graph node-level fault information extracted from 1D vibration signals.
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