The study of neuronal activity with low frequency has shown an increasing interest for its greater stability and reliability recent years. One challenge in analyzing this kind of activity is to find similarities and differences between signals efficiently and effectively. The traditional analysis methods, such as short-time Fourier transform, are easily obscured by background noises and often involve a large number of parameters. Therefore, this paper introduces a novel time-frequency analysis method based on wavelet transformation and half-ellipsoid modeling to extract instantaneous frequency and instantaneous phase information. This method overcomes some shortcomings of conventional time-frequency analysis. In this method, wavelet transformation is used to provide high-level representations of raw signals, and parsimonious half-ellipsoid models are used to extract changes in time domain and frequency domain of neural recordings. The method was validated to local field potentials (LFPs) of olfactory bulb of anesthetized rats during three different odor stimuli. The results suggested that this method could detect odor-relevant features from olfactory signals with large variability. The Odors then were classified with support vector machine (SVM) algorithm and the classification accuracy reached 79.4%.
This study aims to compare two kinds of modified poly (lactic acid) (PLA) materials:PLA-chitosan (PLA-CTS) and PLA-poly (glycolic acid) (PLA-PGA). PLA-CTS and PLA-PGA scaffolds were prepared and observed under electron microscope. The scaffold porosity was calculated and the pH of the degradation solution was measured. Then rat olfactory ensheathing cells (OECs) were cultivated, and mixed cultured respectively with two scaffolds as two groups. The proliferation, adhesion rate and growth condition of the OECs were observed and compared between the two groups. Results showed that both the prepared PLA-CTS and PLA-PGA scaffolds were three-dimensional porous structure and the porosity of PLA-CTS was 91%, while that of PLA-PGA was 87%. The pH of degradation solution decreased gradually, of which PLA-PGA fell faster than PLA-CTS. After added to the two scaffolds, most OECs could grow well, and there were no significant differences between the two groups on MTT test and nuclei number determined by fluorescent microscope. However, the cell adhesion rate of PLA-CTS group was significantly higher than that of PLA-PGA. It can be concluded that compared with PLA-PGA, PLA-CTS might be a better choice as OECs scaffold.
ObjectiveTo investigate the effect of microsurgical therapy and the key techniques in resection of large bilateral olfactory groove meningiomas via unilateral subfrontal approach.MethodsThe clinical data and follow-up results of 181 patients with large bilateral olfactory groove meningiomas who underwent microsurgical removal between June 2007 and May 2014 were retrospectively analyzed. The initial symptom was headache or (and) dizzy in 95 cases, hyposmia or anosmia in 53, impairment of visual function or (and) visual field deficits in 26, cognitive deficits in 3, epilepsy in 2, and accidentally discovered in 2. Unilateral subfrontal approach was applied in all patients (neuroendoscopy was applied in some cases in the later period); incision of falxcerebri, and when necessary, ligation of superior sagittal sinus to resect contralateral tumor were performed. Simultaneous or staged period resection of tumors invading skull base such as ethmoid sinus and superior meatus and reconstruction of the skull base were performed.ResultsThere was no perioperative mortality. Simpson grade Ⅰ resection was obtained in 33 cases, grade Ⅱ resection was in 141, grade Ⅲ resection was in 4, and grade Ⅳ resection was in 3. Among the 229 eyes with preoperative visual impairment, postoperative visual improvement was found in 215 eyes, unimproved was in 12, and aggravation was in 2. The 59 sides which lost their function of olfactory nerve before surgery obtained no recoveries after surgery, while olfactory nerve with residual function preoperative still kept sensing after surgery in 149 lateral sides (149/303). The patients were followed up for (76.9±43.8) months, and postoperative recurrence or residual tumor growth were found in 21 cases.ConclusionsBecause of the contralateral ocular and (or) nasal compensation, early discovery is very difficult for patients with olfactory groove meningioma. The unilateral subfrontal approach can provide sufficient exposure for resection of large bilateral olfactory groove meningiomas and improve the visual acuity and visual field deficits. But it is poor at the preservation of olfactory function. The approach, better with the aid of neuroendoscopy, can improve the total cutting rate, with the advantages of minimally invasion and fewer complications. It is a worthy priority for these tumors.
Studying the ability of the brain to recognize different odors is of great significance in the assessment and diagnosis of olfactory dysfunction. The wavelet energy moment (WEM) was proposed as a feature of olfactory electroencephalogram (EEG) signal and used for odor classification. Firstly, the olfactory evoked EEG data of 13 odors were collected by an experiment. Secondly, the WEM was extracted from olfactory evoked EEG data as the signal feature, and the power spectrum density (PSD), approximate entropy, sample entropy and wavelet entropy were used as the contrast features. Finally, k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF) and decision tree classifier were used to identify different odors. The results showed that using the above four classifiers, the classification accuracy of WEM feature was higher than other features, and the k-NN classifier combined with WEM feature had the highest classification accuracy (91.07%). This paper further explored the characteristics of different EEG frequency bands, and found that most of the classification accuracy based on the features of γ band was better than that of the full band and other bands, among which the WEM feature of the γ band combined with the k-NN classifier had the highest classification accuracy (93.89 %). The research results of this paper could provide a new objective basis for the evaluation of olfactory function. On the other hand, it could also provide new ideas for the study of olfactory-induced emotions.