Animal-fMRI is a powerful method to understand neural mechanisms of cognition, but it remains a major challenge to scan actively participating small animals under low-stress conditions. Here, we present an event-related functional MRI platform in awake pigeons using single-shot RARE fMRI to investigate the neural fundaments for visually-guided decision making. We established a head-fixated Go/NoGo paradigm, which the animals quickly learned under low-stress conditions. The animals were motivated by water reward and behavior was assessed by logging mandibulations during the fMRI experiment with close to zero motion artifacts over hundreds of repeats. To achieve optimal results, we characterized the species-specific hemodynamic response function. As a proof-of-principle, we run a color discrimination task and discovered differential neural networks for Go-, NoGo-, and response execution-phases. Our findings open the door to visualize the neural fundaments of perceptual and cognitive functions in birds—a vertebrate class of which some clades are cognitively on par with primates.
Oscillations of electroencephalographic signals represent the cognitive processes arose from the behavioral task and sensory representations across the mental state activity. Previous studies have shown the relation between event-related EEG and sensory-cognitive representation and revealed that categorization of presented object can be successfully recognized using recorded EEG signals when subjects view objects. Here, EEG signals in conjunction with a multivariate pattern recognition technique were used for investigating the possibility to identify conceptual representation based on the presentation of 12 semantic categories of objects (5 exemplars per category). Using multivariate stimulus decoding methods, surprisingly, we demonstrate that how objects are discriminated from phase pattern of EEG signals across the time in low-frequency band (1-4 Hz), but not from power of oscillatory brain signals in the same frequency band. In contrast, discrimination accuracy from the power of EEG signals has significantly higher than the performance from phase of EEG signal in the high-frequency band (20-30 Hz). Moreover, our results indicate that how the accuracy of prediction changes between various areas of brain continuously across the time. In particular, we find that, during the object categorization task, the inter-trial phase coherence in low-frequency band is significantly higher than other frequency in various regions of interests. This measure is associated with decoding pattern across the time. These results suggest that the mechanism underlying conceptual representation can be mediated by the phase of oscillatory neural activity.
In the last two decades, the avian hippocampus has been repeatedly studied with respect to its architecture, neurochemistry, and connectivity pattern. We review these insights and conclude that we unfortunately still lack proper knowledge on the interaction between the different hippocampal subregions. To fill this gap, we need information on the functional connectivity pattern of the hippocampal network. These data could complement our structural connectivity knowledge. To this end, we conducted a resting-state fMRI experiment in awake pigeons in a 7-T MR scanner. A voxel-wise regression analysis of blood oxygenation level-dependent (BOLD) fluctuations was performed in 6 distinct areas, dorsomedial (DM), dorsolateral (DL), triangular shaped (Tr), dorsolateral corticoid (CDL), temporo-parieto-occipital (TPO), and lateral septum regions (SL), to establish a functional connectivity map of the avian hippocampal network. Our study reveals that the system of connectivities between CDL, DL, DM, and Tr is the functional backbone of the pigeon hippocampal system. Within this network, DM is the central hub and is strongly associated with DL and CDL BOLD signal fluctuations. DM is also the only hippocampal region to which large Tr areas are functionally connected. In contrast to published tracing data, TPO and SL are only weakly integrated in this network. In summary, our findings uncovered a structurally otherwise invisible architecture of the avian hippocampal formation by revealing the dynamic blueprints of this network.
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