Alzheimer’s disease (AD) is a neurodegenerative disease with main symptoms of chronic primary memory loss and cognitive impairment. The study aim was to investigate the correlation between intrahippocampal functional connectivity (FC) and MRI radiomic features in AD. A total of 67 AD patients and 44 normal controls (NCs) were enrolled in this study. Using the seed-based method of resting-state functional MRI (rs-fMRI), the whole-brain FC with bilateral hippocampus as seed was performed, and the FC values were extracted from the bilateral hippocampus. We observed that AD patients demonstrated disruptive FC in some brain regions in the left hippocampal functional network, including right gyrus rectus, right anterior cingulate and paracingulate gyri, bilateral precuneus, bilateral angular gyrus, and bilateral middle occipital gyrus. In addition, decreased FC was detected in some brain regions in the right hippocampal functional network, including bilateral anterior cingulate and paracingulate gyri, right dorsolateral superior frontal gyrus, and right precentral gyrus. Bilateral hippocampal radiomics features were calculated and selected using the A.K. software. Finally, Pearson’s correlation analyses were conducted between these selected features and the bilateral hippocampal FC values. The results suggested that two gray level run-length matrix (RLM) radiomic features and one gray level co-occurrence matrix (GLCM) radiomic feature weakly associated with FC values in the left hippocampus. However, there were no significant correlations between radiomic features and FC values in the right hippocampus. These findings present that the AD group showed abnormalities in the bilateral hippocampal functional network. This is a prospective study that revealed the weak correlation between the MRI radiomic features and the intrahippocampal FC in AD patients.

Since the embryo, the nervous system and immune system have been interacting to regulate each other’s development and working together to resist harmful stimuli. However, oversensitive neural response and uncontrolled immune attack are major causes of various diseases, especially in barrier organs, while neural-immune interaction makes it worse. As the first defense line, the barrier organs give a guarantee to maintain homeostasis in external environment. And the dense nerve innervation and abundant immune cell population in barrier organs facilitate the neuroimmune interaction, which is the physiological basis of multiple neuroimmune-related diseases. Neuroimmune-related diseases often have complex mechanisms and require a combination of drugs, posing challenges in finding etiology and treatment. Therefore, it is of great significance to illustrate the specific mechanism and exact way of neuro-immune interaction. In this review, we first described the mutual regulation of the two principal systems and then focused on neuro-immune interaction in the barrier organs, including intestinal tract, lungs and skin, to clarify the mechanisms and provide ideas for clinical etiology exploration and treatment.

To satisfy the development trend of a few workers or unmanned production in industries, especially in dangerous mining industry, the safety patrol robot is required to replace the safety inspectors. The challenge is how to model the cognition mechanism of inspectors for the safety patrol. The specific problems involve the cognition modeling scheme, the aliasing of the commonly used empirical mode decomposition (EMD) of the electroencephalograph (EEG) filtering, the multisource EEG feature vector construction, and the brain-inspired modeling method. To this end, this article focuses on the perception feature and cognition model applied to safety patrol robot in mining industry. First, the inspector's cognition modeling scheme is designed by using brain-computer interface. Second, a filtering algorithm is developed by embedding the sample entropy and independent component into the EMD. Third, a multisource EEG feature vector is fused by using the power spectral density and the EEG map and the functional brain connectivity. Fourth, the cognition model is built by a convolutional neural network embedded the inception module. The experiments indicate that the modeling scheme is effective. The developed filtering algorithm increases the signal-to-noise ratio by 4.16%. The integrated model reaches the average accuracy of 88.17%.