Cortical information flow during flexible sensorimotor decisions

Abstract

During flexible behavior, multiple brain regions encode sensory inputs, the current task, and choices. It remains unclear how these signals evolve. We simultaneously recorded neuronal activity from six cortical regions (MT, V4, IT, LIP, PFC and FEF) of monkeys reporting the color or motion of stimuli. Following a transient bottom-up sweep, there was a top-down flow of sustained task information from frontoparietal to visual cortex. Sensory information flowed from visual to parietal and prefrontal cortex. Choice signals developed simultaneously in frontoparietal regions and travelled to FEF and sensory cortex. This suggests that flexible sensorimotor choices emerge in a frontoparietal network from the integration of opposite flows of sensory and task information.

A Source for Feature-Based Attention in the Prefrontal Cortex

“…However, the critical question for this study was where the feature selection effects emerged the earliest, and that appears to be VPA. Consistent with our findings, a recent study in which monkeys reported the color or motion of foveally presented stimuli found that choice signals developed in lateral prefrontal cortex and parietal regions and were fed back to FEF and sensory cortex (Siegel et al, 2015). …”

Temporal coding of reward-guided choice in the posterior parietal cortex

“…84 units for monkey C and 43 units for monkey R) showed significant SFC with the nearby LFP during the baseline epoch (the 500 ms preceding the target onset), and 119 units (79%; 78 units for monkey C and 41 units for monkey R) showed significant SFC during the choice epoch (200-700 ms after target onset). We found prominent SFC with a peak in the beta-frequency range (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [permutation tests, familywise error probability (P FWER ) < 0.05] (Fig. 2 A and B).…”

“…Firing rates of neurons in the PPC encode movement intention and the temporal evolution of movement choices (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) as well as decision variables such as expected rewards, the subjective desirability during reward-guided decisions (14)(15)(16)(17)(18), and the certainty in perceptual decisions (19). Decisions are made within a network that extends across many regions of the brain (20)(21)(22)(23)(24)(25), so efficient and flexible mechanisms are required to enable distributed computations. Dynamic and frequency-specific correlations in activity between brain areas are ubiquitous and offer potential physiological mechanisms supporting distributed computations in decision networks (13,21,(26)(27)(28)(29)(30)(31)(32)(33)(34).…”

The modular and integrative functional architecture of the human brain

“…Finally, empirical functional connectivity studies have shown that modules are weakly functionally connected to each other, likely because their computations are predominantly distinct, suggesting modular function (6,7,16,36,79). However, connections do occur between modules that transfer information between the modules and influence local activity in the modules, potentially increasing the modules' computational loads (6,7,16,27,28,36,(80)(81)(82)(83). Thus, to validate the autonomous nature of modules, it is necessary to demonstrate that, as more modules are engaged simultaneously and more information is generated across the network and transferred between the modules, the modules' computational loads do not increase (50,51,53) (Fig.…”