Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD. Methods: T2-weighted images from 69 PD patients and 69 age-and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group. Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986-0.9833) and 0.7732 (95% CI: 0.6292-0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066-0.9469) and 0.7143 (95% CI: 0.5540-0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05). Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.

Autophagy, or type II programmed cell death, plays a crucial role in many nervous system diseases. However, few studies have examined the role of autophagy in post-traumatic stress disorder (PTSD), and the mechanisms underlying PTSD are poorly understood. The objective of this research was to explore the expression of three important autophagy-related proteins, Beclin-1, microtubule-associated protein 1 light chain 3 (LC3), and p62/SQSTM1 (p62), in the medial prefrontal cortex (mPFC) of an animal model of PTSD to identify changes in autophagic activity during PTSD pathogenesis. PTSD was induced in rats by exposure to a single-prolonged stress (SPS). The Morris water maze was used to assess cognitive changes in rats from the SPS and control groups. Transmission electron microscopy (TEM) was employed to observe mPFC morphological changes. Immunohistochemistry, immunofluorescence, and Western blotting techniques were used to detect expression of Beclin-1, LC3, and p62 in the mPFC. The Morris water maze test results showed that the escape latency time was increased and that the percent time in the target quadrant was decreased in the SPS group compared with that in the control group. Numerous visible autolysosomes in mPFC neurons were observed using TEM after SPS stimulation. Compared with that in the control group, the expression of Beclin-1 and the LC3-II/I ratio significantly decreased at 1 day, then increased and peaked at 7 days, and slightly decreased at 14 days after SPS stimulation, whereas the converse was found for p62 expression. In conclusion, dysregulation of autophagic activity in the mPFC may play a crucial role in PTSD pathogenesis.

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The present study was designed to investigate the protective effects of leonurine, a compound purified from Herba Leonuri that is active on ischemic rat behavior and cortical neurons, and explore the underlying mechanism. The general rat activity, cortical neuron morphology, superoxide dismutase (SOD), malondialdehyde (MDA), gaminobutyric acid (GABA) and glutamate decarboxylase 67 (GAD67) levels were measured. We found leonurine significantly improve the general activity of rats in an open-field test, which was associated with attenuated neuronal damage induced by ischemia. Moreover, serum SOD activity was significantly greater, MDA level lower in the leonurine group as compared with ischemia group. In addition, GABA content in the cerebral cortex was significantly greater in high-dose leonurine group. Correspondingly, GAD67 protein level coincided with the GABA level. Taken together, our results demonstrated that leonurine attenuated brain injury during ischemia via antioxidative and anti-excitotoxicity effects by targeting GABA and leonurine might become a useful adjuvant neuroprotective agent.

BackgroundRadiomics is characterized by high-throughput extraction of texture features from medical images and the mining of information that can potentially be used to define neuroimaging markers in many neurological or psychiatric diseases. However, there have been few studies concerning MRI radiomics in post-traumatic stress disorder (PTSD). The study's aims were to appraise changes in microstructure of the medial prefrontal cortex (mPFC) in a PTSD animal model, specifically single-prolonged stress (SPS) rats, by using MRI texture analysis. The feasibility of using a radiomics approach to classify PTSD rats was examined.MethodsMorris water maze and elevated plus maze were used to assess behavioral changes in the rats. Two hundred and sixty two texture features were extracted from each region of interest in T2-weighted images. Stepwise discriminant analysis (SDA) and LASSO regression were used to perform feature selection and radiomics signature building to identify mPFC radiomics signatures consisting of optimal features, respectively. Receiver operating characteristic curve plots were used to evaluate the classification performance. Immunofluorescence techniques were used to examine the expression of glial fibrillary acidic protein (GFAP) and neuronal nuclei (NeuN) in the mPFC. Nuclear pycnosis was detected using 4′,6-diamidino-2-phenylindole (DAPI) staining.ResultsBehavioral results indicated decreased learning and spatial memory performance and increased anxiety-like behavior after SPS stimulation. SDA analysis showed that the general non-cross-validated and cross-validated discrimination accuracies were 86.5% and 80.4%. After LASSO dimensionality reduction, 10 classification models were established. For classifying PTSD rats between the control and each SPS group, these models achieved AUCs of 0.944, 0.950, 0.959, and 0.936. Among four SPS groups, the AUCs were 0.927, 0.943, 0.967, 0.916, 0.932, and 0.893, respectively. The number of GFAP-positive cells and intensity of GFAP-IR within the mPFC increased 1 day after SPS treatment, and then decreased. The intensity of NeuN-IR and number of NeuN-positive cells significantly decreased from 1 to 14 days after SPS stimulation. The brightness levels of DAPI-stained nuclei increased in SPS groups.ConclusionNon-invasive MRI radiomics features present an efficient and sensitive way to detect microstructural changes in the mPFC after SPS stimulation, and they could potentially serve as a novel neuroimaging marker in PTSD diagnosis.