Elevated Gamma Connectivity in Nidopallium Caudolaterale of Pigeons during Spatial Path Adjustment

Abstract

Previous studies showed that spatial navigation depends on a local network including multiple brain regions with strong interactions. However, it is still not fully understood whether and how the neural patterns in avian nidopallium caudolaterale (NCL), which is suggested to play a key role in navigation as a higher cognitive structure, are modulated by the behaviors during spatial navigation, especially involved path adjustment needs. Hence, we examined neural activity in the NCL of pigeons and explored the local field potentials' (LFPs) spectral and functional connectivity patterns in a goal-directed spatial cognitive task with the detour paradigm. We found the pigeons progressively learned to solve the path adjustment task when the learned path was blocked suddenly. Importantly, the behavioral changes during the adjustment were accompanied by the modifications in neural patterns in the NCL. Specifically, the spectral power in lower bands (1-4 Hz and 5-12 Hz) decreased as the pigeons were tested during the adjustment. Meanwhile, an elevated gamma (31-45 Hz and 55-80 Hz) connectivity in the NCL was also detected. These results and the partial least square discriminant analysis (PLS-DA) modeling analysis provide insights into the neural activities in the avian NCL during the spatial path adjustment, contributing to understanding the potential mechanism of avian spatial encoding. This study suggests the important role of the NCL in spatial learning, especially path adjustment in avian navigation.

Keywords: functional connectivity; gamma; nidopallium caudolaterale; path adjustment; pigeon; spatial navigation.

Figure 1

Implanting locations, microelectrode arrays, and a pigeon with implanted electrodes. (a) Diagram of the implanting location and microelectrode array. AP: anteroposterior; ML: mediolateral; DV: dorsoventral. (b) A pigeon (pigeon ID: P097) with implanted electrodes’ matrix.

Figure 2

The apparatus and procedures (one of the sessions as an example). (a) Diagram of the maze apparatus for pigeons and the goal-directed spatial task. The pigeon was trained to learn a preferred path to the goal firstly. After the acquisition, this path was blocked and the pigeon had to adjust the path to access the goal, exploring in the maze to find a new path. Finally, it learned a new path, recovering from the adjustment. (b) The acquisition, adjustment, and recovery phases of the experiment. (c) Experimental task procedures visualizing the learning of the pigeon.

Figure 3

Average time spent and path length walked by pigeons across different sessions. Error bars indicate standard error (number of pigeons: 6; total trials for acquisition, adjustment, and recovery: 525, 133, 620, respectively). Significant differences are indicated by star marks (*** p < 0.001). (a) Average time. (b) Average path length.

Figure 4

Spectral analysis of the neural activity during the spatial task. (a) Examples of simultaneous local field potentials recorded from the nidopallium caudolaterale and filtered at delta, theta, beta, slow-gamma, and fast-gamma frequency bands (calibration bar: 500 µV, 500 ms; from the pigeon numbered P097). (b) Mean power spectrum curves of three phases (number of pigeons: 6; trials for acquisition, adjustment, and recovery: 525, 133, 620, respectively) corresponding to different bands (delta, 1–4 Hz; theta, 5–12 Hz; beta, 13–30 Hz; slow-gamma, 31–45 Hz; fast-gamma, 55–80 Hz).

Figure 5

The heat maps of the coherence coefficient matrices and binarized functional networks for different bands in NCL; (a) delta; (b) theta; (c) beta; (d) slow-gamma; (e) fast-gamma. In the heat maps, the rows and columns of the matrices indicate the channel indexes of NCL. The coefficient value is represented by the color; as it increased, the color appeared to turn yellow and, conversely, turn blue. In the networks, each hollow circle indicates a channel, and the fewer connections between channels, the sparser the connections of the functional networks.

Figure 6

Fitted normal density distribution curves of the coherence coefficient matrices data and radar map of the expected values for different bands. (a) Fitted normal density distribution curves. (b) Radar map of the expected values.

Figure 7

Comparative analysis of network topological properties during different phases for all sessions in five bands. Significant differences are indicated by star marks (*** p < 0.001); (a) delta; (b) theta; (c) beta; (d) slow-gamma; (e) fast-gamma.

Figure 8

Multivariate PLS-DA modeling analysis of the three phases, acquisition to recovery. (a) The normalized average connectivity feature distribution of the three phases. (b) The projected distribution of all samples on the model’s first and second principal components. (c) The loading weights of each original feature on the latent variables corresponding to the first component.