Neuroscience
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Multicenter Study
Deep learning-based segmentation of acute ischemic stroke MRI lesions and recurrence prediction within 1 year after discharge: A multicenter study.
To explore the performance of deep learning-based segmentation of infarcted lesions in the brain magnetic resonance imaging (MRI) of patients with acute ischemic stroke (AIS) and the recurrence prediction value of radiomics within 1 year after discharge as well as to develop a model incorporating radiomics features and clinical factors to accurately predict AIS recurrence. ⋯ The MRA-UNet model can effectively improve the segmentation accuracy of MRI. The model, which was established by combining radiomics features and clinical factors, held some value for predicting AIS recurrence within 1 year.
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Heart failure (HF) frequently suffers from brain abnormalities and cognitive impairments. This study aims to investigate brain structure and function alteration in patients with chronic HF. This retrospective study included 49 chronic HF and 49 health controls (HCs). ⋯ Decreased GMV showed positive correlations with cognitive performance (r = 0.025-0.577, p = 0.025-0.001), while decreased fractional anisotropy was negatively correlated with anxiety scores (r = -0.339, p = 0.040) in patients with chronic HF. This study revealed that patients with chronic HF exhibited brain structure injury affecting gray matter and white matter, as well as FC abnormalities of brain regions responsible for cognition, sensorimotor and visual function. These findings suggest GMV could serve as a neuroimaging biomarker for cognitive impairments and a potential target for neuroprotective therapies in patients with chronic HF.
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The conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is related to various factors. The causal relationships among these factors remain unclear. This study aims to investigate pathways of the progression by using causal analysis and build a predictive model with high accuracy. ⋯ Our study elucidated the initiating factors and three independent pathways involved in the conversion of MCI to AD. The predictive value of each factor was clarified and a multi-predictor nomogram was established with high accuracy.
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Loneliness is intricately connected to social cognition, yet the precise brain mechanisms that underscore their relationship need further exploration. The present study employed a theory of mind processing task that engaged participants in assessing the trajectories of geometric shapes while undergoing fMRI scans. The comprehensive data pool encompassed loneliness assessments and brain imaging data from a cohort of 157 participants. ⋯ Furthermore, functional connectivity among the social network, the default mode network, and somatomotor networks emerged as crucial factors in prediction. Brain regions contributed strongly in prediction are involved in a variety of social cognitive processes, including intention inference, empathy, and information integration. The results illuminate the association between brain functional connectivity induced by social cognition and loneliness, which enhance the comprehensive understanding of this complex emotional state and may have implications for its diagnosis and intervention.