Introducing a Comprehensive Framework to Measure Spike-LFP Coupling

Measuring the coupling of single neuron's spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC), pairwise phase consistency (PPC), and spike-triggered correlation matrix synchronization (SCMS). Synchronization is generally quantified using the PLV and SFC. PLV and SFC methods are either biased on the spike rates or the number of trials. To resolve these problems the PPC measure has been introduced. However, there are some shortcomings associated with the PPC measure which is unbiased only for very high spike rates. However evaluating spike-LFP phase coupling (SPC) for short trials or low number of spikes is a challenge in many studies. Lastly, SCMS measures the correlation in terms of phase in regions around the spikes inclusive of the non-spiking events which is the major difference between SCMS and SPC. This study proposes a new framework for predicting a more reliable SPC by modeling and introducing appropriate machine learning algorithms namely least squares, Lasso, and neural networks algorithms where through an initial trend of the spike rates, the ideal SPC is predicted for neurons with low spike rates. Furthermore, comparing the performance of these three algorithms shows that the least squares approach provided the best performance with a correlation of 0.99214 and R2 of 0.9563 in the training phase, and correlation of 0.95969 and R2 of 0.8842 in the test phase. Hence, the results show that the proposed framework significantly enhances the accuracy and provides a bias-free basis for small number of spikes for SPC as compared to the conventional methods such as PLV method. As such, it has the general ability to correct for the bias on the number of spike rates

Classification of Epileptic EEG Signals by Wavelet based CFC

Electroencephalogram, an influential equipment for analyzing humans activities and recognition of seizure attacks can play a crucial role in designing accurate systems which can distinguish ictal seizures from regular brain alertness, since it is the first step towards accomplishing a high accuracy computer aided diagnosis system (CAD). In this article a novel approach for classification of ictal signals with wavelet based cross frequency coupling (CFC) is suggested. After extracting features by wavelet based CFC, optimal features have been selected by t-test and quadratic discriminant analysis (QDA) have completed the Classification.Comment: Electroencephalogram; Wavelet Decomposition; Cross Frequency Coupling;Quadratic Discriminant Analysis; T-test Feature Selectio

The effect of depth variation on size and catch rate of green tiger shrimp, Penaeus semisulcatus (De Haan, 1884) in Bushehr coastal waters, Northern Persian Gulf

Catch data of Penaeus semisulcatus were collected for three years (2009-2011) during the period of June to August to estimate the effect of depth variation on length frequency and catch rate (Kg h-1) of the species. No difference was observed between the shallow and deeper catch rate (ANOVA test, P > 0.05). However, the size composition of green tiger shrimp were affected by depth variation (Kolmogorov-Smirnov test, P < 0.05) and were positively correlated with depth (P < 0.05 and r = 0.140). The smaller individuals prefer shallow waters, indicating behaviour differences between the juveniles and adults of this species. The positive correlation between size of shrimp and water depth can be attributed to the behaviour predation.Keywords: Green tiger shrimp, Penaeus semisulcatus, catch rate, depth variation, Persian GulfAfrican Journal of Biotechnology Vol. 12(20), pp. 3058-306

Brain Electrical Stimulation for Animal Navigation

The brain stimulation and its widespread use is one of the most important subjects in studies of neurophysiology. In brain electrical stimulation methods, following the surgery and electrode implantation, electrodes send electrical impulses to the specific targets in the brain. The use of this stimulation method is provided therapeutic benefits for treatment chronic pain, essential tremor, Parkinsons disease, major depression, and neurological movement disorder syndrome (dystonia). One area in which advancements have been recently made is in controlling the movement and navigation of animals in a specific pathway. It is important to identify brain targets in order to stimulate appropriate brain regions for all the applications listed above. An animal navigation system based on brain electrical stimulation is used to develop new behavioral models for the aim of creating a platform for interacting with the animal nervous system in the spatial learning task. In the context of animal navigation the electrical stimulation has been used either as creating virtual sensation for movement guidance or virtual reward for movement motivation. In this paper, different approaches and techniques of brain electrical stimulation for this application has been reviewed. Keywords: Rat Robot, Brain Computer Interface, Electrical Stimulation, Cyborg Intelligence, Brain to Brain InterfaceComment: in Fars

Effects of long-term exposure to amoxicillin residues on stress resistance and body compositions of Penaeus vannamei

Background and Objective: Occurrence of the pharmaceutical active residues (particularly antibiotics) threatens the health of the environment and human society. Therefore, this research aimed to investigate the impacts of the Amoxicillin (AMX) residues on resistance to environmental stress and biochemical compositions of the body in Penaeus vannamei. Materials and Methods: Six-hundred specimens with a mean (±SD) weight and total length of 9.23±1.77 g and 9.28±0.73 cm were randomly experimented in four triplicate treatments, namely T1(control): without AMX residues in a rearing environment, T2: 100 μg/L AMX residues concentration in water, T3: 300 μg/L and T4: 500 μg/L for 60 days. At the end of the experimental trial, five specimens for biochemical body composition analyses were separately sampled. Ten shrimps from each treatment were also randomly selected and exposed to 50 ppt salinity stress for 48 hours, and then survival rates were computed. Results: Body composition analyses showed that moisture and protein not differed among the treatments (p>0.05), while fat in T2 (28.29±5.50) was significantly more than in others (p<0.05). The lowest values of ash were obtained in T1 and T4, and they differed with T2 and T3 (p<0.05). The highest survival rate of shrimps exposed to salinity stress (50 ppt in 48 h) was observed in T2 and T3, in contrast, the lowest value was recorded for T4 (p<0.01). Conclusion: Findings of the present research indicate that the occurrence of high doses of AMX residues pollution in the rearing water affects the stress resistance of P. vannamei which can be due to disruption of protein and fat metabolisms in the shrimp body

Editorial: The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain networks

Editorial on the Research Topic -The new frontier in brain network physiology: from temporal dynamics to the principles of integration in physiological brain network

The association between the outcomes of trauma, education and some socio-economic indicators

Background: There are many debates on socioeconomic indicators influencing trauma outcomes.Objectives: This study aimed to determine the association between education as a socioeconomic indicator and trauma outcomes.Methods: This descriptive-analytical study was conducted on 30,448 trauma patients during 2016-2021. The data were based on the minimum dataset of the National Trauma Registry of Iran (NTRI) from six different trauma centers in various cities of the country. The variables used in this study included age, education level, marital status, cause of injury, Glasgow Coma Scale (GCS), intensive care unit (ICU) admission, Injury Severity Score (ISS), and in-hospital mortality. Logistic regression was used to investigate the association between independent variables and trauma outcomes.Results: The study included 30,448 trauma patients with male predominance (75.8%). The mean age was 36.9 years. The most frequent education level was secondary education, with 14,228 (46.6%). Education levels had significant relationships with ISS, death, and ICU admission (P<0.001). Moreover, after applying the multiple logistic regression, the odds of deaths for trauma patients with no formal, primary, and secondary education levels were 3.36, 5.03, and 3.65 times, respectively, more than the odds of deaths at the higher education level after controlling for other factors (all Ps<0.05). However, there were no such relationships between education levels and the odds of ICU admission.Conclusion: Findings of the present study showed a significant association between the education levels and trauma outcomes. Adjusted for other covariates, the chance of death for trauma patients with no formal, primary, or secondary education levels was higher than that at the higher education level

Frequency modulation of cortical rhythmicity governs behavioral variability, excitability and synchrony of neurons in the visual cortex

Abstract Research in cognitive neuroscience has renewed the idea that brain oscillations are a core organization implicated in fundamental brain functions. Growing evidence reveals that the characteristic features of these oscillations, including power, phase and frequency, are highly non-stationary, fluctuating alongside alternations in sensation, cognition and behavior. However, there is little consensus on the functional implications of the instantaneous frequency variation in cortical excitability and concomitant behavior. Here, we capitalized on intracortical electrophysiology in the macaque monkey’s visual area MT performing a visuospatial discrimination task with visual cues. We observed that the instantaneous frequency of the theta–alpha oscillations (4–13 Hz) is modulated among specific neurons whose RFs overlap with the cued stimulus location. Interestingly, we found that such frequency modulation is causally correlated with MT excitability at both scales of individual and ensemble of neurons. Moreover, studying the functional relevance of frequency variations indicated that the average theta–alpha frequencies foreshadow the monkey’s reaction time. Our results also revealed that the neural synchronization strength alters with the average frequency shift in theta–alpha oscillations, suggesting frequency modulation is critical for mutually adjusting MTs’ rhythms. Overall, our findings propose that theta–alpha frequency variations modulate MT’s excitability, regulate mutual neurons’ rhythmicity and indicate variability in behavior

Evaluation of Phase Locking and Cross Correlation Methods for Estimating the Time Lag between Brain Sites: A Simulation Approach

Introduction: Direction and latency of electrical connectivity between different sites of brain explains brain neural functionality. We compared efficiency of cross correlation and phase locking methods in time lag estimation which are based on local field potential (LFP) and LFPspike signals, respectively. Methods: Signals recorded from MT area of a macaque’s brain was used in a simulation approach. The first signal was real brain activity and the second was identical to the first one, but with two kinds of delayed and not delayed forms. Time lag between two signals was estimated by cross correlation and phase locking methods. Results: Both methods estimated the time lags with no errors. Phase locking was not as time efficient as correlation. In addition, phase locking suffered from temporal self bias. Discussion: Correlation was a more efficient method. Phase locking was not considered as a proper method to estimate the time lags between brain sites due to time inefficiency and self bias, the problems which are reported for the first time about this method

Decoding of visual attention from LFP signals of macaque MT.

The local field potential (LFP) has recently been widely used in brain computer interfaces (BCI). Here we used power of LFP recorded from area MT of a macaque monkey to decode where the animal covertly attended. Support vector machines (SVM) were used to learn the pattern of power at different frequencies for attention to two possible positions. We found that LFP power at both low (<9 Hz) and high (31-120 Hz) frequencies contains sufficient information to decode the focus of attention. Highest decoding performance was found for gamma frequencies (31-120 Hz) and reached 82%. In contrast low frequencies (<9 Hz) could help the classifier reach a higher decoding performance with a smaller amount of training data. Consequently, we suggest that low frequency LFP can provide fast but coarse information regarding the focus of attention, while higher frequencies of the LFP deliver more accurate but less timely information about the focus of attention