Using multimodal MRI to investigate alterations in brain structure and function in the BBZDR/Wor rat model of type 2 diabetes

Neuroanatomical and functional consequences of oxytocin treatment at birth in prairie voles

Birth is a critical period for the developing brain, a time when surging hormone levels help prepare the fetal brain for the tremendous physiological changes it must accomplish upon entry into the 'extrauterine world'. A number of obstetrical conditions warrant manipulations of these hormones at the time of birth, but we know little of their possible consequences on the developing brain. One of the most notable birth signaling hormones is oxytocin, which is administered to roughly 50% of laboring women in the United States prior to / during delivery. Previously, we found evidence for behavioral, epigenetic, and neuroendocrine consequences in adult prairie vole offspring following maternal oxytocin treatment immediately prior to birth. Here, we examined the neurodevelopmental consequences in adult prairie vole offspring following maternal oxytocin treatment prior to birth. Control prairie voles and those exposed to 0.25 mg/kg oxytocin were scanned as adults using anatomical and functional MRI, with neuroanatomy and brain function analyzed as voxel-based morphometry and resting state functional connectivity, respectively. Overall, anatomical differences brought on by oxytocin treatment, while widespread, were generally small, while differences in functional connectivity, particularly among oxytocin-exposed males, were larger. Analyses of functional connectivity based in graph theory revealed that oxytocin-exposed males in particular showed markedly increased connectivity throughout the brain and across several parameters, including closeness and degree. These results are interpreted in the context of the organizational effects of oxytocin exposure in early life and these findings add to a growing literature on how the perinatal brain is sensitive to hormonal manipulations at birth.

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients' cognitive function and early intervention is helpful to improve patient's quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer's disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM. Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM). Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index. Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers. Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

Potential new therapeutic target for Alzheimer's disease: Glucagon-like peptide-1

Increasing evidence shows a close relationship between Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM). Recently, glucagon-like peptide-1 (GLP-1), a gut incretin hormone, has become a well-established treatment for T2DM and is likely to be involved in treating cognitive impairment. In this mini review, the similarities between AD and T2DM are summarised with the main focus on GLP-1-based therapeutics in AD.