Objective: We aimed to evaluate the activity of the human glymphatic system in type 2 diabetes mellitus (T2DM) using diffusion tensor image analysis along with the perivascular space (DTI-ALPS). Methods: Diffusion tensor images were acquired to calculate the diffusivities in the direction of the x-axis (right-to-left; Dx), y-axis (anterior-to-posterior; Dy), and z-axis (inferior-to-superior; Dz) of the plane of the lateral ventricle body in 20 patients with type 2 diabetes and 10 people in a control group. We evaluated the diffusivity along with the perivascular spaces, as well as the projection fibers and association fibers, separately. The analysis along the perivascular space (ALPS-index) was defined as the mean (Dxpro, Dypro)/mean (Dypro, Dzasc), where the Dxpro and Dxasc were the Dx values in the projection and association fiber areas, respectively. Results: There were significant differences in diffusivity along the projection fibers and the association fibers among the groups. The significant differences among the groups along the perivascular spaces, shown as the ALPS-index and medical history of T2DM, indicating lower water diffusivity along the perivascular space concerning type 2 diabetes severity, was also observed. Conclusion: Lower diffusivity along the perivascular space on DTI-APLS can reflect impairment of the glymphatic system in T2DM. This study showed that the activity of the glymphatic system could be evaluated by diffusion tensor image analysis.
Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.
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