Efforts to understand the functional architecture of the brain have consistently identified multiple overlapping large-scale neural networks that are observable across multiple states. Despite the ubiquity of these networks, it remains unclear how regions within these large-scale neural networks interact to orchestrate behavior. Here, we collected functional magnetic resonance imaging data from 188 human subjects who engaged in three cognitive tasks and a resting-state scan. Using multiple tasks and a large sample allowed us to use split-sample validations to test for replication of results. We parceled the task-rest pairs into functional networks using a probabilistic spatial independent components analysis. We examined changes in connectivity between task and rest states using dual-regression analysis, which quantifies voxelwise connectivity estimates for each network of interest while controlling for the influence of signals arising from other networks and artifacts. Our analyses revealed systematic state-dependent functional connectivity in one brain region: the precuneus. Specifically, task performance led to increased connectivity (compared to rest) between the precuneus and the left frontoparietal network (lFPN), whereas rest increased connectivity between the precuneus and the default-mode network (DMN). The absolute magnitude of this effect was greater for DMN, suggesting a heightened specialization for resting-state cognition. All results replicated within the two independent samples. Our results indicate that the precuneus plays a core role not only in DMN, but also more broadly through its engagement under a variety of processing states.

The core feature of an economic exchange is a decision to trade one good for another, based on a comparison of relative value. Economists have long recognized, however, that the value an individual ascribes to a good during decision making (i.e., their relative willingness to trade for that good) does not always map onto the reward they actually experience. Here, we show that experienced value and decision value are represented in distinct regions of ventromedial prefrontal cortex (VMPFC) during the passive consumption of rewards. Participants viewed two categories of rewards-images of faces that varied in their attractiveness and monetary gains and losses-while being scanned using functional magnetic resonance imaging. An independent market task, in which participants exchanged some of the money that they had earned for brief views of attractive faces, determined the relative decision value associated with each category. We found that activation of anterior VMPFC increased with increasing experienced value, but not decision value, for both reward categories. In contrast, activation of posterior VMPFC predicted each individual's relative decision value for face and monetary stimuli. These results indicate not only that experienced value and decision value are represented in distinct regions of VMPFC, but also that decision value signals are evident even in the absence of an overt choice task. We conclude that decisions are made by comparing neural representations of the value of different goods encoded in posterior VMPFC in a common, relative currency.

Dopaminergic networks modulate neural processing across a spectrum of function from perception to learning to action. Multiple organizational schemes based on anatomy and function have been proposed for dopaminergic nuclei in the midbrain. One schema originating in rodent models delineated ventral tegmental area (VTA), implicated in complex behaviors like addiction, from more lateral substantia nigra (SN), preferentially implicated in movement. However, because anatomy and function in rodent midbrain differs from the primate midbrain in important ways, the utility of this distinction for human neuroscience has been questioned. We asked whether functional definition of networks within the human dopaminergic midbrain would recapitulate this traditional anatomical topology. We first developed a method for reliably defining SN and VTA in humans at conventional MRI resolution. Hand-drawn VTA and SN regions-of-interest (ROIs) were constructed for 50 participants, using individually-localized anatomical landmarks and signal intensity. Individual segmentation was used in seed-based functional connectivity analysis of resting-state functional MRI data; results of this analysis recapitulated traditional anatomical targets of the VTA versus SN. Next, we constructed a probabilistic atlas of the VTA, SN, and the dopaminergic midbrain region comprised (SN plus VTA) from individual hand-drawn ROIs. The combined probabilistic (VTA plus SN) ROI was then used for connectivity-based dual-regression analysis in two independent resting-state datasets (n=69 and n=79). Results of the connectivity-based, dual-regression functional segmentation recapitulated results of the anatomical segmentation, validating the utility of this probabilistic atlas for future research.

During the course of daily activity, our level of engagement with the world varies on a moment-to-moment basis. Although these fluctuations in vigilance have critical consequences for our thoughts and actions, almost nothing is known about the neuronal substrates governing such dynamic variations in task engagement. We investigated the hypothesis that the posterior cingulate cortex (CGp), a region linked to default-mode processing by hemodynamic and metabolic measures, controls such variations. We recorded the activity of single neurons in CGp in 2 macaque monkeys performing simple tasks in which their behavior varied from vigilant to inattentive. We found that firing rates were reliably suppressed during task performance and returned to a higher resting baseline between trials. Importantly, higher firing rates predicted errors and slow behavioral responses, and were also observed during cued rest periods when monkeys were temporarily liberated from exteroceptive vigilance. These patterns of activity were not observed in the lateral intraparietal area, an area linked to the frontoparietal attention network. Our findings provide physiological confirmation that CGp mediates exteroceptive vigilance and are consistent with the idea that CGp is part of the ''default network'' of brain areas associated with control of task engagement.default network ͉ lateral intraparietal cortex ͉ working memory ͉ task engagement ͉ attention T he neural mechanisms supporting our engagement with the outside world remain poorly understood. Many studies have implicated the activation of a dorsal frontoparietal network of brain regions in selective attention and the consequent benefits in reaction time and accuracy in task performance (1-3). Recent studies suggest that a complementary network of brain areas, known as the default network, is deactivated during elevated task engagement (4-7). The default network comprises several brain regions that show a high metabolic and hemodynamic activity at rest that is suppressed during goal-directed tasks (3)(4)(5)(7)(8)(9)(10)(11). A subset of these regions, including the posterior cingulate cortex and ventromedial prefrontal cortex, shows resting metabolic and hemodynamic activity that is significantly higher than the global mean (5-6, 9).Deactivation of the default network has been implicated in attention, arousal, and task engagement. Increased hemodynamic response in the default network predicts occasional lapses in attention (12), failures to encode memories (13), and failures to perceive near-threshold somatosensory stimuli (14). Variations in the activity of the default network have been linked to self-directed cognition (4, 15), episodic memory retrieval (13), environmental monitoring (16), and motivated behavior (11). These data suggest that the default network may track moment-to-moment variations in the balance of exteroceptive vigilance and interoceptive cognition.Despite these observations, the precise contribution of the default network, and the posterior cingulate cortex (CGp) spec...

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent components analysis (ICA). We estimated voxelwise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity.