Cell-Type Specific Burst Firing Interacts with Theta and Beta Activity in Prefrontal Cortex During Attention States.

Population-level theta and beta band activity in anterior cingulate and prefrontal cortices (ACC/PFC) are prominent signatures of self-controlled, adaptive behaviors. But how these rhythmic activities are linked to cell-type specific activity has remained unclear. Here, we suggest such a cell-to-systems level linkage. We found that the rate of burst spiking events is enhanced particularly during attention states and that attention-specific burst spikes have a unique temporal relationship to local theta and beta band population-level activities. For the 5-10 Hz theta frequency range, bursts coincided with transient increases of local theta power relative to nonbursts, particularly for bursts of putative interneurons. For the 16-30 Hz beta frequency, bursts of putative interneurons phase synchronized stronger than nonbursts, and were associated with larger beta power modulation. In contrast, burst of putative pyramidal cells showed similar beta power modulation as nonbursts, but were accompanied by stronger beta power only when they occurred early in the beta cycle. These findings suggest that in the ACC/PFC during attention states, mechanisms underlying burst firing are intimately linked to narrow band population-level activities, providing a cell-type specific window into rhythmic inhibitory gating and the emergence of rhythmically coherent network states during goal directed behavior.

A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque.

Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.

Comparison of orientation maps obtained with different number of stimulus orientations.

The structure of orientation maps computed from a different number of stimulus orientations was studied in visual cortical area 18 of the cat. Single condition maps (SCMs) were obtained to 16 stimulus orientations, of which angle maps were generated using 4, 8, and 16 SCMs corresponding to multiples of 45, 22.5, and 11.25 degrees, respectively. The overall orientation distribution of the three types of maps was compared on a pixel-by-pixel basis. Twenty percent of the pixels of the 4-orientations maps differed by more than +/-17 degrees from those produced by 16 orientations. Maps of 8 orientations differed by 6.4 and 5.8% from those of 4 and 16 orientations, respectively. Structural differences between the maps were mainly found at locations displaying high rate of change in orientation preference, i.e., orientation centers and adjoining short, fracture-like zones. These changes included lateral shifts up to 155 microm (average: 38.7 microm) in the position of orientation centers and appearance/disappearance of orientation centers when compared between different conditions. In general, these changes were three times more frequent between maps of 4/8 and 4/16 orientations than 8/16 orientations. It is concluded that orientation maps should be calculated from activity maps representing 8 or more stimulus orientations.