Predictive Modeling of Rat Brain Local Field Potentials using Single-Variable and Multivariable Approaches

Accurate prediction of neural dynamics in the brain's reward circuitry is crucial for elucidating how natural and pharmacological rewards influence neural activity and connectivity. Traditional linear models, such as autoregressive (AR) and vector autoregressive (VAR), often inadequately capture the inherent nonlinear interactions in neural data. This study develops and benchmarks both linear and advanced deep learning models for predicting local field potentials (LFPs) in the rat hippocampus (HIP) and nucleus accumbens (NAc) across morphine, food, and saline conditions. We compared AR, VAR, long short-term memory (LSTM), and wavelet-based deep learning model (WCLSA). Additionally, a novel wavelet coherence-enhanced model (WCOH CLSA) was introduced to capture cross-region connectivity. Results indicate that WCLSA achieves superior predictive accuracy (up to 0.97 for HIP in food, 0.96 for NAc in morphine), while VAR performs competitively in the food group due to significant HIP-NAc correlation. Wavelet coherence analysis reveals robust connectivity in natural reward contexts and disrupted or nonlinear relationships under pharmacological influence. These findings highlight the differential engagement of HIP and NAc in reward processing and underscore the importance of advanced nonlinear models for capturing complex neural dynamics. The study provides a robust framework for predictive neuroscience and elucidates functional interactions within the reward circuitry.

Applications of wavelet transform in classification of local field potential recorded from the rat brain in conditioned place preference paradigm

This study investigates the multi-label classification of Local Field Potential (LFP) data from the hippocampus (HIP) and nucleus accumbens (NAc) in the rat brain, focusing on reward responses using the Conditioned Place Preference (CPP) paradigm. Rats were conditioned with saline, morphine, and food rewards, and LFP recordings were conducted from both HIP and NAc during pre- and post-tests. The LFP data were classified into four categories: treatment types, test phases, recording channels, and chamber positions within the CPP setup. Features were extracted using Continuous Wavelet Transform (CWT), Wavelet Coherence, and Wavelet Scattering. Classification was performed via Decision Trees, Multilayer Perceptrons, and Support Vector Machines. Notably, in the Food group, HIP and combined HIP-NAc features yielded the highest classification accuracy for CPP chambers, whereas NAc features excelled in the Morphine group. Employing wavelet scattering, an 80% classification accuracy was achieved across treatment groups, test phases, and channels. Exceptionally high classification accuracies were observed for Food-post-test-HIP (99.75%) and Morphine-post-test-NAc (99.58%). The study reveals that NAc activity is pivotal for morphine-induced CPP, whereas HIP and HIP-NAc connectivity are crucial for food-induced CPP. The proposed methodology provides a novel avenue for precisely classifying LFP data, shedding light on neural circuit activities underlying behavioral responses.

A Global Data-Driven Model for The Hippocampus and Nucleus Accumbens of Rat From The Local Field Potential Recordings

In brain neural networks, Local Field Potential (LFP) signals represent the dynamic flow of information. Analyzing LFP clinical data plays a critical role in improving our understanding of brain mechanisms. One way to enhance our understanding of these mechanisms is to identify a global model to predict brain signals in different situations. This paper identifies a global data-driven based on LFP recordings of the Nucleus Accumbens and Hippocampus regions in freely moving rats. The LFP is recorded from each rat in two different situations: before and after the process of getting a reward which can be either a drug (Morphine) or natural food (like popcorn or biscuit). A comparison of five machine learning methods including Long Short Term Memory (LSTM), Echo State Network (ESN), Deep Echo State Network (DeepESN), Radial Basis Function (RBF), and Local Linear Model Tree (LLM) is conducted to develop this model. LoLiMoT was chosen with the best performance among all methods. This model can predict the future states of these regions with one pre-trained model. Identifying this model showed that Morphine and natural rewards do not change the dynamic features of neurons in these regions.

Time-domain Classification of the Brain Reward System: Analysis of Natural- and Drug-Reward Driven Local Field Potential Signals in Hippocampus and Nucleus Accumbens

Addiction is a major public health concern characterized by compulsive reward-seeking behavior. The excitatory glutamatergic signals from the hippocampus (HIP) to the Nucleus accumbens (NAc) mediate learned behavior in addiction. Limited comparative studies have investigated the neural pathways activated by natural and unnatural reward sources. This study has evaluated neural activities in HIP and NAc associated with food (natural) and morphine (drug) reward sources using local field potential (LFP). We developed novel approaches to classify LFP signals into the source of reward and recorded regions by considering the time-domain feature of these signals. Proposed methods included a validation step of the LFP signals using autocorrelation, Lyapunov exponent and Hurst exponent to assess the meaningful stability of these signals (lack of chaos). By utilizing the probability density function (PDF) of LFP signals and applying Kullback-Leibler divergence (KLD), data were classified to the source of the reward. Also, HIP and NAc regions were visually separated and classified using the symmetrized dot pattern technique, which can be applied in real-time to ensure the deep brain region of interest is being targeted accurately during LFP recording. We believe our method provides a computationally light and fast, real-time signal analysis approach with real-world implementation.

Improvement of the Izhikevich model based on the rat basolateral amygdala and hippocampus neurons, and recognition of their possible firing patterns

Introduction: Identifying the potential firing patterns following by different brain regions under normal and abnormal conditions increases our understanding of what is happening in the level of neural interactions in the brain. On the other hand, it is important to be capable of modeling the potential neural activities, in order to build precise artificial neural networks. The Izhikevich model is one of the simple biologically plausible models that is capable of capturing the most known firing patterns of neurons. This property makes the model efficient in simulating large-scale networks of neurons. Improving the Izhikevich model for adapting with the neuronal activity of rat brain with great accuracy would make the model effective for future neural network implementations. Methods: Data sampling from two brain regions, the HIP and BLA, is performed by extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter. Further data analyses are done through NeuroExplorer and MATLAB software. In order to optimize the Izhikevich model parameters, the genetic algorithm is used. Results: In the present study, the possible firing patterns of the real single neurons of the HIP and BLA are identified. Additionally, improvement of the Izhikevich model is achieved. As a result, the real neuronal spiking pattern of these regions neurons, and the corresponding cases of the Izhikevich neuron spiking pattern are adjusted with great accuracy. Conclusion: This study is conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large scale neural networks simulations, as well as reducing the modeling complexity. This aim is achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns and eliminating unrealistic ones, as the results of this study.