Toward an Improvement of the Analysis of Neural Coding

Empirical mode decomposition of local field potential data from optogenetic experiments

Introduction

This study investigated the effects of cocaine administration and parvalbumin-type interneuron stimulation on local field potentials (LFPs) recorded in vivo from the medial prefrontal cortex (mPFC) of six mice using optogenetic tools.

Methods

The local network was subject to a brief 10 ms laser pulse, and the response was recorded for 2 s over 100 trials for each of the six subjects who showed stable coupling between the mPFC and the optrode. Due to the strong non-stationary and nonlinearity of the LFP, we used the adaptive, data-driven, Empirical Mode Decomposition (EMD) method to decompose the signal into orthogonal Intrinsic Mode Functions (IMFs).

Results

Through trial and error, we found that seven is the optimum number of orthogonal IMFs that overlaps with known frequency bands of brain activity. We found that the Index of Orthogonality (IO) of IMF amplitudes was close to zero. The Index of Energy Conservation (IEC) for each decomposition was close to unity, as expected for orthogonal decompositions. We found that the power density distribution vs. frequency follows a power law with an average scaling exponent of ~1.4 over the entire range of IMF frequencies 2-2,000 Hz.

Discussion

The scaling exponent is slightly smaller for cocaine than the control, suggesting that neural activity avalanches under cocaine have longer life spans and sizes.

Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants

Implants are now the standard method of replacing missing or damaged teeth. Despite the improving technologies for the manufacture of implants and the introduction of new protocols for diagnosing, planning, and performing implant placement operations, the percentage of complications in the early postoperative period remains quite high. In this regard, there is a need to develop new methods for preliminary assessment of the patient's condition to predict the success of single implant survival. The intensive development of artificial intelligence technologies and the increase in the amount of digital information that is available for analysis make it relevant to develop systems based on neural networks for auxiliary diagnostics and forecasting. Systems based on artificial intelligence in the field of dental implantology can become one of the methods for forming a second opinion based on mathematical decision making and forecasting. The actual clinical evaluation of a particular case and further treatment are carried out by the dentist, and AI-based systems can become an integral part of additional diagnostics. The article proposes an artificial intelligence system for analyzing various patient statistics to predict the success of single implant survival. As the topology of the neural network, the most optimal linear neural network architectures were developed. The one-hot encoding method was used as a preprocessing method for statistical data. The novelty of the proposed system lies in the developed optimal neural network architecture designed to recognize the collected and digitized database of various patient factors based on the description of the case histories. The accuracy of recognition of statistical factors of patients for predicting the success of single implants in the proposed system was 94.48%. The proposed neural network system makes it possible to achieve higher recognition accuracy than similar neural network prediction systems due to the analysis of a large number of statistical factors of patients. The use of the proposed system based on artificial intelligence will allow the implantologist to pay attention to the insignificant factors affecting the quality of the installation and the further survival of the implant, and reduce the percentage of complications at all stages of treatment. However, the developed system is not a medical device and cannot independently diagnose patients. At this point, the neural network system for analyzing the statistical factors of patients can predict a positive or negative outcome of a single dental implant operation and cannot be used as a full-fledged tool for supporting medical decision-making.

Medium spiny neurons activity reveals the discrete segregation of mouse dorsal striatum

Behavioral studies differentiate the rodent dorsal striatum (DS) into lateral and medial regions; however, anatomical evidence suggests that it is a unified structure. To understand striatal dynamics and basal ganglia functions, it is essential to clarify the circuitry that supports this behavioral-based segregation. Here, we show that the mouse DS is made of two non-overlapping functional circuits divided by a boundary. Combining in vivo optopatch-clamp and extracellular recordings of spontaneous and evoked sensory activity, we demonstrate different coupling of lateral and medial striatum to the cortex together with an independent integration of the spontaneous activity, due to particular corticostriatal connectivity and local attributes of each region. Additionally, we show differences in slow and fast oscillations and in the electrophysiological properties between striatonigral and striatopallidal neurons. In summary, these results demonstrate that the rodent DS is segregated in two neuronal circuits, in homology with the caudate and putamen nuclei of primates.

Multiscale dynamics of interstimulus interval integration in visual cortex

Although the visual cortex receives information at multiple temporal patterns, much of the research in the field has focused only on intervals shorter than 1 second. Consequently, there is almost no information on what happens at longer temporal intervals. We have tried to address this question recording neuronal populations of the primary visual cortex during visual stimulation with repetitive grating stimuli and intervals ranging from 1 to 7 seconds. Our results showed that firing rate and response stability were dependent of interval duration. In addition, there were collective oscillations with different properties in response to changes in intervals duration. These results suggest that visual cortex could encode visual information at several time scales using oscillations at multiple frequencies.