Objective To investigate the effect of fast-aging on the excitability of hippocampus CA1 neurons in mouse and the possible interaction between fast-aging and hippocampus. Methods Using brain slice and extracellular recording technique to record the firing of hippocampus CA1 neurons in fast-aging(SA M-P/8) and normal control mice, after preprocessing, neural firing train were obtained. Using neural firing rate and inter-spike-inter to investigate interaction between fast-aging and hippocampal neural firing. Results The neural firing rate of hippocampus CA1 neurons in fast-aging mice is (1.052±0.364) Hz(n=14), while the neural firing ratein normal control mice is (4.416±1.306) Hz(n=22). In fast-aging mice, 80.5% inter spike intervel(ISI) is longerthan 1sec, but in normal control mice, 95.6% ISI is shorter than 0.5sec. Conclusion The decreased firing rate of hippocampus CA1 and longer ISI observed in the fast-aging mice indicates that fast-aging significantly inhibit hippocampal CA1 neurons excitability.

Objective Toinvestigatetheentropyoflocalfieldpotentials(LFPs)recordedinratmedialprefrontal cortex during a Y-maze working memory (WM) task, to provide computing support for neural coding mechanism.Methods Sixteen-channel LFPs were recorded from SD rats while they performed a Y-maze WM task.The data came from 4 rats, 20 trials (10 correct trials and 10 incorrect trials) per rat provided by laboratory of neurobiology in medicine,Tianjin Medical University.Original LFPs were preprocessed to remove 50 Hz power line noise and baseline drift.Multi-taper Fourier transform was applied to calculate spatial distributions of LFPs and band pass filter were used to extract characteristic signal.The entroy coding of 16 channel LFPs was as follows: the physiological window was set to be 500 ms, the step length of physiological window was set to be 125 ms, windows were added to LFPs data, and then LFPs entropy of each sliding window was computed and averaged to get the trend of multichannel entropy values duringthe WM task.Results The power of θ band (4-12 Hz) in LFPs increased.The averaged entropy value ofmultichannel θ band LFPs in correct trials was 0.939±-0.020, which were larger than those in the resting state, 0.795±0.031 (P＜0.05).Those during wrong WM task had no significant difference, which didn't encode the WM task.Conclusions The principal frequency band related to WM is the θ band and LFPs entropy encodes the WM effectively.

To study the effects of surfactants on wettability of excipients, the contact angles of six types of surfactants on the surface of two common excipients and mixture of three surfactants with excipients were measured using hypsometry method. The results demonstrated that contact angle of water on the surface of excipients was associated with hydrophilcity of excipients. Contact angle was lowered with increase in hydrophilic groups of excipient molecules. The sequence of contact angle from small to large was starch < sodium benzoate < polyvinylpyrrolidone < sodium carboxymethylcellulose < sodium alginate < chitosan < hydroxypropyl methyl cellulose

Objective:To investigate the synchronized feature patterns of local field potentials in the hippocampus (HPC) and prefrontal cortex (PFC) during working memory based on time-varying spectral coherence so as to support the study of information processing mechanisms in working memory.Methods:The local field potentials (LFPs) signals of the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC) were collected from six SD rats during the performance of a spatial working memory task in the Y-maze, and the time-frequency distributions of vHPC and mPFC LFPs were calculated by applying the short-time Fourier transform (STFT) to determine the characteristic frequency bands of the working memory and then to investigate the synchronized patterns of vHPC and mPFC LFPs based on the coherent of the time-varying frequency spectrum. Finally, support vector machines were applied to explore the feasibility of applying spectral coherence values to predict working memory.Results:When rats performed working memory tasks correctly, the energy of the theta band (4 - 12 Hz) of the HPC and PFC increased (all P < 0.01), and the spectral coherence value of the theta band of the HPC-PFC increased ( P < 0.05). Support vector machine training and prediction using the average peak spectral coherence and the difference between the peak and the onset when correctly and incorrectly executing the working memory as features resulted in 89% accuracy, 90% precision, 88% recall, and 88% F1 scores, all of which were statistically significant differences compared to the results of the randomly disrupted labeled data rearranging (all P < 0.05). Conclusions:Synchronized synergy in the HPC-PFC theta band is one of the potential mechanisms for correctly performing information processing in working memory.

Objective:To analyze the electroencephalogram (EEG) features of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI), and to combine the characteristics for classification and prediction.Methods:One hundred and thirty-five patients attending the Department of Neurology at the General Hospital of Tianjin Medical University were enrolled, including 34 patients with AD, 67 patients with MCI, and 34 healthy control (HC). The electroencephalogram signals of these patients in the resting state were collected and preprocessed. Relative power spectral density features and sample entropy features on a multi-band scale were extracted to compare the whole-brain differences in electroencephalogram features among the 3 groups of subjects, and then subdivided into brain regions and individual leads for in-depth analysis. The above two features were fused to classify and predict AD, MCI, and HC by support vector machine (SVM).Results:The frontal regions had higher δ relative power spectral densities than the other regions, and the occipital and temporal regions showed relatively lower distributions. θ-Band relative power spectral densities had a more even distribution of sizes across brain regions. α-Band relative power spectral densities were concentrated in the occipital lobe, while β-band relative power spectral densities were mainly concentrated in the parietal and temporal lobes. Except for the central lobe, the δ-band relative power spectral densities of the AD group were higher than those of the MCI group ( P < 0.05) and HC group ( P < 0.01) in all brain regions and the whole brain. θ-band relative power spectral densities of the AD group were higher than those of the MCI gourp ( P < 0.001) and HC group ( P < 0.001) in the whole brain and in all brain regions. α-Band relative power spectral densities of the AD group were lower than those of the other groups only in the temporal lobe (all P < 0.05). The relative power spectral density of the β-band in the AD group was higher than that of the other groups in the whole brain and in all brain regions ( P < 0.05, 0.01, 0.001). The difference in the relative power spectral density of the δ-band in the C3 lead in the central lobe of the AD and HC groups was statistically significant ( P < 0.05). The relative power spectral density of the γ-band in the temporal lobe was higher than that in the other regions of the AD group, the MCI group, and the HC group. The relative power spectral density of the γ-band in the T3 lead in the AD group was significantly lower than that in the T4 lead. The average entropy of samples in the whole brain and in each brain region was lower than that in the HC group in the AD and MCI groups (all P < 0.05). The entropy of the samples at lead C3 in the AD group was lower than that in the MCI group ( P < 0.05). The differences between the relative power spectral density, sample entropy, and the actual data classification evaluation indexes (accuracy rate, precision rate, recall rate, and F1 score) that fused the two features, and the rearranged data were all statistically significant (all P < 0.001). When the relative power spectral density feature and the sample entropy feature were fused in the classification features, the best classification prediction was achieved, with an accuracy rate of 80%, a precision rate of 78%, a recall rate of 78%, and the F1 score of 79%. Conclusions:Relative power spectral density and sample entropy analysis can reveal the abnormalities of electroencephalogram activities of AD and MCI patients from different perspectives (linear and nonlinear), and the combination of these two features in classification prediction can improve the classification effect.

Anxiety disorder is a common emotional handicap, which seriously affects the normal life of patients and endangers their physical and mental health. The prefrontal cortex is a key brain region which is responsible for anxiety. Action potential and behavioral data of rats in the elevated plus maze (EPM) during anxiety (an innate anxiety paradigm) can be obtained simultaneously by using the and in conscious animal multi-channel microelectrode array recording technique. Based on maximum likelihood estimation (MLE), the action potential causal network was established, network connectivity strength and global efficiency were calculated, and action potential causal network connectivity pattern of the medial prefrontal cortex was quantitatively characterized. We found that the entries (44.13±6.99) and residence period (439.76±50.43) s of rats in the closed arm of the elevated plus maze were obviously higher than those in the open arm [16.50±3.25, <0.001; (160.23±48.22) s, <0.001], respectively. The action potential causal network connectivity strength (0.017 3±0.003 6) and the global efficiency (0.044 2±0.012 8) in the closed arm were both higher than those in the open arm (0.010 4±0.003 2, <0.01; 0.034 8±0.011 4, <0.001), respectively. The results suggest that the changes of action potential causal network in the medial prefrontal cortex are related to anxiety state. These data could provide support for the study of the brain network mechanism in prefrontal cortex during anxiety.