1 research outputs found
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.Comment: 12 pages, 7 figures first two authors contributed equally to this
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