Alterations of Spontaneous Cortical and Subcortical Activity in Type 2 Diabetes Mellitus with and without Depression.

INTRODUCTION: Type 2 diabetes mellitus (T2DM) patients with depression have a higher risk of complications and mortality than T2DM without depression. However, the exact neuropathophysiological mechanism remains unclear. Consequently, the current study aimed to investigate the alteration of cortical and subcortical spontaneous neural activity in T2DM patients with and without depression. METHODS: The demographic data, clinical variables, neuropsychological tests, and functional and anatomical magnetic resonance imaging of depressed T2DM (n = 47) of non-depressed T2DM (n = 59) and healthy controls (n = 41) were collected and evaluated. The correlation analysis, stepwise multiple linear regression, and receiver operating characteristic curve were performed for further analysis. RESULTS: Abnormal neural activities in the bilateral posterior cingulate cortex (PCC) and hippocampus were observed in depressed and non-depressed T2DM and the right putamen of the depressed T2DM. Interestingly, the subcortical degree centrality (DC) of the right hippocampus and putamen were higher in depressed than non-depressed T2DM. Furthermore, the cortical amplitude of low-frequency fluctuation (ALFF) in PCC, subcortical DC in the putamen of depressed T2DM, and hippocampus of non-depressed T2DM was correlated with cognitive scores. In contrast, the cortical fractional ALFF in PCC of non-depressed T2DM was correlated with depression scores. CONCLUSIONS: The abnormalities of spontaneous cortical activity in PCC and subcortical activity in the hippocampus might represent the neurobiological feature of cerebral dysfunction in T2DM. Notably, the altered subcortical activity in the right putamen might mainly associate with negative emotion in T2DM, which could be a promising biomarker for recognizing early cerebral dysfunction in depressed T2DM. This study provided a novel insight into the neuropathophysiological mechanism of brain dysfunction in T2DM with and without depression.

Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis.

Purpose: This study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy. Materials and methods: This study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software. Results: A total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up. Conclusion: This study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392.

Altered thalamic functional connectivity and cerebral blood flow in insomnia disorder: a resting-state functional magnetic resonance imaging study.

BACKGROUND AND PURPOSE: The thalamus plays a crucial role in sleep regulation, but few studies have examined functional connectivity of the thalamus in insomnia disorder. This study aimed to investigate the connectivity patterns and perfusion of the thalamus in patients with insomnia disorder using resting-state functional connectivity and three-dimensional arterial spin labeling (3D ASL). MATERIALS AND METHODS: In total, 56 patients with insomnia disorder and 59 healthy control participants with a similar age-, gender-, and education lever distribution underwent resting-state functional magnetic resonance imaging (rs-fMRI) and 3D-ASL. The thalamus was selected as the seed region. Whole-brain connectivity was assessed using rs-fMRI. Cerebral blood flow (CBF) of the bilateral thalamus was measured with 3D-ASL using region-of-interest (ROI) analysis. All participants completed a series of neuropsychological assessments. Sleep parameters were assessed via polysomnography (PSG). The relationships between imaging parameters and clinical variables were assessed with Pearson correlation analysis. RESULTS: Compared with healthy controls, patients with insomnia disorder exhibited increased connectivity between the left thalamus and right precentral gyrus, and right thalamus and left middle frontal gyrus (MFG), right superior parietal lobule (SPL) and right superior frontal gyrus (SFG). Whereas decreased connectivity was noted between the right thalamus and left posterior cerebellar lobe including Crus I, Crus II, and VII b/VII. Connectivity between the right thalamus and left Crus I was positively correlated with MoCA scores (r = 0.286, P = 0.036) in insomnia disorder. CONCLUSIONS: Our findings illustrate functional abnormalities in brain connectivity and their relationship with cognitive impairments in insomnia disorder, providing novel insight into the neural mechanisms of insomnia disorder.