Analysis of Non-Task State-Specific Rhythms in Nidopallium Caudolaterale of Pigeons

The rhythmic oscillation research of neural signal under different non-task state is crucial to understand the inner information processing and encoding mechanisms of birds, but the difference of specific rhythms is unclear. In this paper, we selected pigeon as model animal and nidopallium caudolaterale (NCL)as the target brain area to implant the microelectrode array. The neural signals under: awake resting, sleep resting and anesthesia state are recorded and analyzed to assess their difference of rhythmic oscillation. Firstly, the power spectral density (PSD)analysis of local field potential (LFP)signal are carried out and compared. Then, functional networks of NCL area are constructed within different frequency band based on coherence calculation. Finally, network topology characteristics are quantitatively analyzed and compared. The results show that power of Delta(0-4Hz)rhythm under sleep resting state is significantly higher than the other states and its network connection is also intenser than others, which shows Delta rhythm may have a close relationship with nighttime sleep. For awake resting state, the power and network connections in the Beta(12-30Hz)and Low-Gamma(30-100Hz)bands are significantly higher than others, which may be related to cognitive thinking in awaking state. Under the anesthesia state, the network topology characteristics results in the Theta(4-12Hz)rhythm show that the network connection intensity and information transmission efficiency are lower than the other states, this rhythmic inhibition may be used to characterize the neural signal pattern of the birds' NCL under anesthesia.

[1]  Amiram Grinvald,et al.  Dynamic Patterns of Spontaneous Ongoing Activity in the Visual Cortex of Anesthetized and Awake Monkeys are Different , 2019, Cerebral cortex.

[2]  Earl K Miller,et al.  Gamma and beta bursts during working memory readout suggest roles in its volitional control , 2017, Nature Communications.

[3]  Revati Shriram,et al.  PSD based Coherence Analysis of EEG Signals for Stroop Task , 2014 .

[4]  Christoph Braun,et al.  Coherence of gamma-band EEG activity as a basis for associative learning , 1999, Nature.

[5]  P. Fries Neuronal gamma-band synchronization as a fundamental process in cortical computation. , 2009, Annual review of neuroscience.

[6]  M. Kawasaki,et al.  Beta phase synchronization in the frontal-temporal-cerebellar network during auditory-to-motor rhythm learning , 2017, Scientific Reports.

[7]  P. Manganotti,et al.  Coherence and Consciousness: Study of Fronto-Parietal Gamma Synchrony in Patients with Disorders of Consciousness , 2014, Brain Topography.

[8]  E. Pastalkova,et al.  Oscillatory patterns in hippocampus under light and deep isoflurane anesthesia closely mirror prominent brain states in awake animals , 2016, Hippocampus.

[9]  Blake S. Porter,et al.  Neurons in the Pigeon Nidopallium Caudolaterale Display Value-Related Activity , 2018, Scientific Reports.

[10]  L. Colgin Mechanisms and functions of theta rhythms. , 2013, Annual review of neuroscience.

[11]  Lino Nobili,et al.  Slow EEG rhythms and inter-hemispheric synchronization across sleep and wakefulness in the human hippocampus , 2012, NeuroImage.

[12]  N. McNaughton,et al.  The frequency of hippocampal theta rhythm is modulated on a circadian period and is entrained by food availability , 2015, Front. Behav. Neurosci..

[13]  N. Logothetis,et al.  Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms , 2013, Neuron.

[14]  V. V. Nikulin,et al.  Phase synchronization between alpha and beta oscillations in the human electroencephalogram , 2006, Neuroscience.