DTI and H(1)-MR spectroscopy can be used to detect early radiation-induced changes of temporal lobe NAWM following radiation therapy for NPC. Metabolic alterations and water diffusion characteristics of temporal lobe NAWM in patients with NPC after RT were dynamic and transient.

Neurocognitive dysfunction of varying degrees is common in patients with hepatitis B virus-related cirrhosis (HBV-RC) without overt hepatic encephalopathy (OHE). However, the neurobiological mechanisms underlying these dysfunctions are not well understood. We sought to identify changes in the neural activity of patients with HBV-RC without OHE in the resting state by using the amplitude of low-frequency fluctuation (ALFF) method and to determine whether these changes were related to impaired cognition. Resting-state functional MRI data from 30 patients with HBV-RC and 30 healthy controls matched for age, sex, and years of education were compared to determine any differences in the ALFF between the two groups. Cognition was measured with the psychometric hepatic encephalopathy score (PHES), and the relationship between these scores and ALFF variation was assessed. Compared with controls, patients showed widespread lower standardized ALFF (mALFF) values in visual association areas (bilateral lingual gyrus, middle occipital gyrus, and left inferior temporal gyrus), motor-related areas (bilateral precentral gyrus, paracentral lobule, and right postcentral gyrus), and the default mode network (bilateral cuneus/precuneus and inferior parietal lobule). Higher mALFF values were found in the bilateral orbital gyrus/rectal gyrus. In patients, mALFF values were significantly positive correlated with the PHES in the right middle occipital gyrus and bilateral precentral gyrus. Our findings of resting-state abnormalities in patients with HBV-RC without OHE suggest that neurocognitive dysfunction in patients with HBV-RC without OHE may be caused by abnormal neural activity in multiple brain regions.

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.

In current society, speech recognition can perform a variety of functions, such as completing voice commands, enabling speech processing, spoken language translation and facilitating communication. Therefore, the study of speech recognition technology is of high value. However, current speech recognition techniques focus on among clearly expressed spoken words, which poses great challenges for recognition with spoken pronunciation or dialect pronunciation. Some scholars currently use a model combining time-delay neural networks and long and short-term memory networks to build speech recognition systems, but the performance in acoustic recognition is poor. Therefore, the study proposes a convolutional neural network (CNN), time-delay neural network (TDNN) and output-gate projected Gated recurrent by analyzing the deep neural network unit (OPGRU) combined with a composite English speech recognition model. The model can optimize the acoustic model after the introduction of CNN, and the model can accurately recognize pronunciation features and make the model have a wider recognition range. The proposed composite model is compared with the Word error rate (Wer) and runtime metrics in the Mozilla Common Voice dataset. The Wer result of the composite model is 23.42% and the running time is 1418 s. The Wer result of the composite model is 24.61% and the running time is 1385 s. Compared with the TDNN-OPGRU model, the Wer of the composite model decreases by 1.19% but the running time increases by 33 s. The accuracy of the composite model is higher than that of the TDNN-OPGRU model. From a comprehensive consideration, the speech recognition model accuracy has higher priority, so the composite model proposed in the study has better performance.