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. 2014 Jul 2;83(1):216-25.
doi: 10.1016/j.neuron.2014.05.005. Epub 2014 Jun 12.

Increases in functional connectivity between prefrontal cortex and striatum during category learning

Affiliations

Increases in functional connectivity between prefrontal cortex and striatum during category learning

Evan G Antzoulatos et al. Neuron. .

Abstract

Functional connectivity between the prefrontal cortex (PFC) and striatum (STR) is thought critical for cognition and has been linked to conditions like autism and schizophrenia. We recorded from multiple electrodes in PFC and STR while monkeys acquired new categories. Category learning was accompanied by an increase in beta band synchronization of LFPs between, but not within, the PFC and STR. After learning, different pairs of PFC-STR electrodes showed stronger synchrony for one or the other category, suggesting category-specific functional circuits. This category-specific synchrony was also seen between PFC spikes and STR LFPs, but not the reverse, reflecting the direct monosynaptic connections from the PFC to STR. However, causal connectivity analyses suggested that the polysynaptic connections from STR to the PFC exerted a stronger overall influence. This supports models positing that the basal ganglia "train" the PFC. Category learning may depend on the formation of functional circuits between the PFC and STR.

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Figures

Figure 1
Figure 1
Task design. A. The schematic illustrates the time course of a single trial. The animal had to respond to a randomly presented exemplar by choosing between a saccade to the right or left targets (green squares). B. Two example categories. New pairs of prototypes (top) were constructed for each recording session. Distortion of each prototype gave rise to hundreds of unique exemplars (only 2 of which are shown for each category). C. Average behavioral performance (% correct) ± SEM across recording sessions. The animals started by learning a few individual SR associations (SR Learning stage: always the first 2 blocks). As they progressed through the blocks, they were trained on more and more exemplars (Category Learning stage), until they eventually learned the categories and their behavior stabilized (Category Performance stage). The Category Learning and Category Performance stages are shown for illustration only: the timing of each could vary across recording sessions, based on the animals’ performance on each new set of categories. (Adapted from Antzoulatos and Miller, 2011.)
Figure 2
Figure 2
Frequency-specific oscillations in PFC and striatum (STR) during two trial epochs (exemplar and decision) across the three stages of learning. A. Average PLV ± SEM as a function of frequency: Peak synchrony between PFC and STR beta-band oscillations (in this and all figures, shaded rectangle indicates the 12-30 Hz beta band), and learning-induced enhancement of this synchrony during the decision epoch (see also Figs. S1, S2, and Table S1). B. Average spectral power (±SEM) in PFC (top) and STR (bottom) is high at the beta band, but does not change across learning stages.
Figure 3
Figure 3
Synchrony between intrinsic pairs of electrodes in PFC and STR. A. Average PLV (±SEM): Although intrinsic connectivity peaked at the beta band both in PFC (top) and in STR (bottom), it did not change with learning (see also Fig. S2). B. The percent increase of synchrony between PFC and STR after the SR Learning stage during the decision epoch (right) was significantly greater than the corresponding change in synchrony of intrinsic PFC pairs (PFC-PFC) and STR pairs (STR-STR) of electrodes.
Figure 4
Figure 4
Analyses of PFC-STR synchrony in error trials of SR Learning and Category Learning stages. A. Average (±SEM) PLV in error trials: In contrast to the increase of beta-band synchrony observed in correct trials (Fig. 2), synchrony between PFC and STR did not increase across learning stages; rather, it decreased significantly at least during the exemplar epoch. B. The average (±SEM) z-transformed difference (d’) between error- and correct-trial PLV. During both trial epochs, error trials displayed stronger PFC-STR synchrony than did correct trials in the SR Learning stage, but, in the Category Learning stage, this difference was either eliminated (decision epoch) or reversed (exemplar epoch).
Figure 5
Figure 5
Category selectivity in the strength of PFC-STR synchrony. A. Synchrony between PFC and STR LFPs (average z-transformed d’ ±SEM) displayed significant category selectivity during the Category Performance stage. Category-specific synchrony was observed at the beta band during the exemplar epoch and the delta band during the decision epoch. B. Similar to the LFP-LFP synchrony above, MUA-LFP synchrony (average z-transformed d’ ± SEM) between PFC-STR (spikes in PFC, LFP in STR; top) also displayed significant category selectivity at the beta band of the exemplar epoch: Again this was evident for the first time during the Category Performance stage. In contrast, the reverse direction (spikes in STR and LFP in PFC; bottom) did not show any category selectivity (see also Fig. S3).
Figure 6
Figure 6
Analyses of Granger causal connectivity between PFC and STR. A. Average Granger connectivity index ± SEM: The two directions of causal connectivity during the two trial epochs of the SR Learning stage. Striatum exerts stronger influence on the prefrontal LFPs (STR->PFC) than the other way around (PFC->STR). This difference is seen across the frequency spectrum, but especially at the beta band (shaded rectangle). B. Average (± SEM) relative causality (STR->PFC direction normalized to the PFC->STR direction) across learning stages. In contrast to the robust enhancement of functional connectivity at the beta band (20 Hz) with learning (Fig. 2), causal connectivity did not increase significantly, suggesting that the relative influence of one area on the other did not change.

References

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