It has been found that in biological studies, the simple linear superposition mathematical model cannot be used to express the feature mapping relationship from multiple activated grid cells' grid fields to a single place cell's place field output in the hippocampus of the cerebral cortex of rodents. To solve this problem, people introduced the Gauss distribution activation function into the area. We in this paper use the localization properties of the function to deal with the linear superposition output of grid cells' input and the connection weights between grid cells and place cells, which filters out the low activation rate place fields. We then obtained a single place cell field which is consistent with biological studies. Compared to the existing competitive learning algorithm place cell model, independent component analysis method place cell model, Bayesian positon reconstruction method place cell model, our experimental results showed that the model on the neurophysiological basis can not only express the feature mapping relationship between multiple activated grid cells grid fields and a single place cell's place field output in the hippocampus of the cerebral cortex of rodents, but also make the algorithm simpler, the required grid cells input less and the accuracy rate of the output of a single place field higher.
This study aims to explore the diagnosis in patients with Alzheimer's disease (AD) based on magnetic resonance (MR) images, and to compare the differences of bilateral hippocampus in classification and recognition. MR images were obtained from 25 AD patients and 25 normal controls (NC) respectively. Three-dimensional texture features were extracted from bilateral hippocampus of each subject. The texture features that existed significant differences between AD and NC were used as the features in a classification procedure. Back propagation (BP) neural network model was built to classify AD patients from healthy controls. The classification accuracy of three methods, which were principal components analysis, linear discriminant analysis and non-linear discriminant analysis, was obtained and compared. The correlations between bilateral hippocampal texture parameters and Mini-Mental State Examination (MMSE) scores were calculated. The classification accuracy of nonlinear discriminant analysis with a neural network model was the highest, and the classification accuracy of right hippocampus was higher than that of the left. The bilateral hippocampal texture features were correlated to MMSE scores, and the relative of right hippocampus was higher than that of the left. The neural network model with three-dimensional texture features could recognize AD patients and NC, and right hippocampus might be more helpful to AD diagnosis.
In the current study, we aim to investigate whether post-traumatic stress disorder (PTSD) is associated with structural alterations in specific subfields of hippocampus comparing with trauma-exposed control (TC) in a relatively large sample. We included 67 PTSD patients who were diagnosed under Diagnostic and Statistical Manual of Mental Disorders (4th Edition) (DSM-Ⅳ) criteria and 78 age- and sex-matched non-PTSD adult survivors who experienced similar stressors. High resolution T1 weighted images were obtained via a GE 3.0 T scanner. The structural data was automatically segmented using FreeSurfer software, and volume of whole hippocampus and subfield including CA1, CA2-3, CA4-DG, fimbria, presubiculum, subiculum and fissure were extracted. Volume differences between the two groups were statistically compared with age, years of education, duration from the events and intracranial volume (ICV) as covariates. Hemisphere, sex and diagnosis were entered as fixed factors. Relationship between morphometric measurements with Clinician-Administered PTSD Scale (CAPS) score and illness duration were performed using Pearson’s correlation with SPSS. Comparing to TC, PTSD patients showed no statistically significant alteration in volumes of the whole hippocampus and all the subfields (P > 0.05). In male patients, there were significant correlations between CAPS score and volume of right CA2-3 ( R2 = 0.197, P = 0.034), right subiculum (R2 = 0.245, P = 0.016), and duration statistically correlated with right fissure (R2 = 0.247, P = 0.016). In female patients, CAPS scores significant correlated with volume of left presubiculum (R2 = 0.095, P = 0.042), left subiculum (R2 = 0.090, P = 0.048), and left CA4-DG (R2 = 0.099, P = 0.037). The main findings of the current study suggest that stress event causes non-selective damage to hippocampus in both PTSD patients and TC, and gender-specific lateralization may underlie PTSD pathology.
In this paper, a new method for the classification of Alzheimer’s disease (AD) using multi-feature combination of structural magnetic resonance imaging is proposed. Firstly, hippocampal segmentation and cortical thickness and volume measurement were performed using FreeSurfer software. Then, histogram, gradient, length of gray level co-occurrence matrix and run-length matrix were used to extract the three-dimensional (3D) texture features of the hippocampus, and the parameters with significant differences between AD, MCI and NC groups were selected for correlation study with MMSE score. Finally, AD, MCI and NC are classified and identified by the extreme learning machine. The results show that texture features can provide better classification results than volume features on both left and right sides. The feature parameters with complementary texture, volume and cortical thickness had higher classification recognition rate, and the classification accuracy of the right side (100%) was higher than that of the left side (91.667%). The results showed that 3D texture analysis could reflect the pathological changes of hippocampal structures of AD and MCI patients, and combined with multi-feature analysis, it could better reflect the essential differences between AD and MCI cognitive impairment, which was more conducive to clinical differential diagnosis.
Elderly patients account for 80% of cardiac arrest patients. The incidence of poor neurological prognosis after return of spontaneous circulation of these patients is as high as 90%, much higher than that of young. This is related to the fact that the mechanism of hippocampal brain tissue injury after ischemia-reperfusion in elderly cardiac arrest patients is aggravated. Therefore, this study reviews the possible mechanisms of poor neurological prognosis after return of spontaneous circulation in elderly cardiac arrest animals, and the results indicate that the decrease of hippocampal perfusion and the number of neurons after resuscitation are the main causes of the increased hippocampal injury, among which oxidative stress, mitochondrial dysfunction and protein homeostasis disorder are the important factors of cell death. This review hopes to provide new ideas for the treatment of elderly patients with cardiac arrest and the improvement of neurological function prognosis through the comparative analysis of elderly and young animals.