Sei-etsu Fujiwara

Cross-correlogram between rat hippocampal and prefrontal neuronal activities.

We recorded spontaneous multiple unit activities (MUAs) of the hippocampus and prefrontal cortex in urethane-anesthetized rats and analyzed cross-correlograms between these MUAs to investigate the functional connectivity of neuronal activities. Results of these analyses reveal a significant correlation between MUAs in these regions, in which the firing initiated from either hippocampus (type H-P) or prefrontal cortex (type P-H) according to the significant peak of cross-correlograms. Furthermore, the MUA bursts were counted: a significant correlation was found between the peak height of cross-correlograms and MUA burst counts in type H-P, but not type P-H. These results suggest that the correlation between the hippocampus and prefrontal cortex MUAs that are related to the burst firing might reflect functional connectivity.

Rat prefrontal response and prestimulation local field potential power in vivo.

To determine whether spontaneous local field potential (LFP) activities in the rat medial prefrontal cortex influence the responses evoked by hippocampal stimulation, we investigated the relationship between the evoked responses and the LFP powers immediately before the stimulation using anesthetized rats. We demonstrated that the degrees of evoked response showed significant inverse correlations with the prefrontal LFP powers in a specific frequency band (including the &ggr; band) immediately before the stimulation. The results indicate that the specific frequency band activities in the prefrontal LFPs may be involved in prefrontal responsiveness. Spontaneous LFP activities may have a role in information processing in the hippocampus–prefrontal cortex pathway.

Rat hippocampus-prefrontal multiple units and synaptic efficacy in vivo.

We recorded multiple unit activities of the CA1 region of the intermediate hippocampus and prelimbic area of the prefrontal cortex, and evoked responses in the prefrontal cortex by hippocampal stimulation in urethane-anesthetized rats. The multiple unit activities between these regions showed significant peaks of cross-correlograms, which indicated that firing initiated mainly from either the hippocampus (type HP) or the prefrontal cortex (type PH). In type HP, the slopes of evoked responses showed a significant inverse correlation with peak heights of cross-correlograms and number of bursts of multiple unit activities. These results suggest that multiple unit activity-based cross-correlograms (a measurement to test functional connectivity) are influenced by both evoked response (synaptic connectivity) and effects of local circuits.

Analysis of local field potentials in the hippocampus of piglets during rest

It is unclear how our brain predicts reward and punishing outcome on perception of ambiguous stimuli in environment. To investigate relationship between perceptually ambiguous stimuli and reward processing on them, we examined reward-predicting brain activity on high and low coherency random dot motion stimuli by using fMRI. First, we trained subjects to establish a contingency between particular direction of motion stimuli and delivery of reward. Then, we presented high and low coherence motion stimuli, and asked subjects to judge direction of the motion. Juice or saliva was followed by stimulus direction associated with reward or neutral outcome, respectively. The data at the time of cue presentation showed that activation in the caudate was correlated with reward prediction based on stimulus direction that was influenced by coherence level, whereas reward-predicting activation based on subject’s performance was observed in the putamen. The results suggested that stimulus-based and perception-based reward prediction could be dissociated in the basal ganglia.

Relationship between prefrontal and hippocampal neuronal spike activities in anesthetized rats

In general, the activity of neurons highly depends on the structure of neural network, namely, the synaptic connections among the neurons. On the other hand, this network structure is also gradually changing, because the synaptic connection is modulated by its plasticity depending on the current neuronal activity. This cooperative dynamics between the neuronal activity and the synaptic connection is essential for realizing high adaptability in brain. However, it is almost unclear what type of functional network structure is organized through such a cooperative dynamics. Thus, we investigate this issue in the modeling study. Considering an oscillatory activity of neurons, we show that the system can be described by the both dynamics of the general phase oscillators and synaptic weights. This system exhibits various types of dynamical behaviors according to the rule of synaptic plasticity.