Developmental Maturation of the Precuneus as a Functional Core of the Default Mode Network.

Efforts to map the functional architecture of the developing human brain have shown that connectivity between and within functional neural networks changes from childhood to adulthood. Although prior work has established that the adult precuneus distinctively modifies its connectivity during task versus rest states [Utevsky, A. V., Smith, D. V., & Huettel, S. A. Precuneus is a functional core of the default-mode network. Journal of Neuroscience, 34, 932-940, 2014], it remains unknown how these connectivity patterns emerge over development. Here, we use fMRI data collected at two longitudinal time points from over 250 participants between the ages of 8 and 26 years engaging in two cognitive tasks and a resting-state scan. By applying independent component analysis to both task and rest data, we identified three canonical networks of interest-the rest-based default mode network and the task-based left and right frontoparietal networks (LFPN and RFPN, respectively)-which we explored for developmental changes using dual regression analyses. We found systematic state-dependent functional connectivity in the precuneus, such that engaging in a task (compared with rest) resulted in greater precuneus-LFPN and precuneus-RFPN connectivity, whereas being at rest (compared with task) resulted in greater precuneus-default mode network connectivity. These cross-sectional results replicated across both tasks and at both developmental time points. Finally, we used longitudinal mixed models to show that the degree to which precuneus distinguishes between task and rest states increases with age, due to age-related increasing segregation between precuneus and LFPN at rest. Our results highlight the distinct role of the precuneus in tracking processing state, in a manner that is both present throughout and strengthened across development.

Reward and executive control network resting-state functional connectivity is associated with impulsivity during reward-based decision making for cocaine users.

BACKGROUND: Cocaine addiction is related to impulsive decision making that is mediated by brain circuitry involved in reward processing and executive functions, such as cognitive control and attentional salience. Resting-state functional connectivity between reward and executive control circuitry is altered among cocaine users, with concomitant deficits in impulsivity and learning. Prior research has examined how select brain regions interact to influence impulsive decision making for drug users; however, research examining interactions between large-scale brain networks and impulsive behavior is limited. METHODS: The current study compared reward and executive control network resting-state functional connectivity and its relationship to impulsive decision making between cocaine users (n = 37) and non-cocaine using control participants (n = 35). Participants completed computerized decision-making tasks and a separate resting-state functional magnetic resonance imaging scan. Data underwent independent component, dual regression, and linear regression moderation analyses. RESULTS: Higher impulsivity on the Balloon Analogue Risk Task (BART) was associated with inverse resting-state connectivity between the left cognitive control and subgenual anterior cingulate extended reward networks for cocaine users, while the opposite was found for controls. Less impulsivity on the monetary choice questionnaire was associated with stronger positive resting-state connectivity between the attentional salience and striatal core reward networks for controls, while cocaine users showed no association between impulsivity and resting-state connectivity of these networks. CONCLUSIONS: Cocaine users show aberrant associations between reward-executive control resting-state network coupling and impulsive decision making. The findings support the conclusion that an imbalance between reward and executive control circuitry contributes to impulsivity in drug use.

Large-Scale Network Coupling with the Fusiform Cortex Facilitates Future Social Motivation.

Large-scale functional networks, as identified through the coordinated activity of spatially distributed brain regions, have become central objects of study in neuroscience because of their contributions to many processing domains. Yet, it remains unclear how these domain-general networks interact with focal brain regions to coordinate thought and action. Here, we investigated how the default-mode network (DMN) and executive control network (ECN), two networks associated with goal-directed behavior, shape task performance through their coupling with other cortical regions several seconds in advance of behavior. We measured these networks' connectivity during an adaptation of the monetary incentive delay (MID) response-time task in which human participants viewed social and nonsocial images (i.e., pictures of faces and landscapes, respectively) while brain activity was measured using fMRI. We found that participants displayed slower reaction times (RTs) subsequent to social trials relative to nonsocial trials. To examine the neural mechanisms driving this subsequent-RT effect, we integrated independent components analysis (ICA) and a network-based psychophysiological interaction (nPPI) analysis; this allowed us to investigate task-related changes in network coupling that preceded the observed trial-to-trial variation in RT. Strikingly, when subjects viewed social rewards, an area of the fusiform gyrus (FG) consistent with the functionally-defined fusiform face area (FFA) exhibited increased coupling with the ECN (relative to the DMN), and the relative magnitude of coupling tracked the slowing of RT on the following trial. These results demonstrate how large-scale, domain-general networks can interact with focal, domain-specific cortical regions to orchestrate subsequent behavior.

Neural mechanisms of social decision-making in the primate amygdala.

Social decisions require evaluation of costs and benefits to oneself and others. Long associated with emotion and vigilance, the amygdala has recently been implicated in both decision-making and social behavior. The amygdala signals reward and punishment, as well as facial expressions and the gaze of others. Amygdala damage impairs social interactions, and the social neuropeptide oxytocin (OT) influences human social decisions, in part, by altering amygdala function. Here we show in monkeys playing a modified dictator game, in which one individual can donate or withhold rewards from another, that basolateral amygdala (BLA) neurons signaled social preferences both across trials and across days. BLA neurons mirrored the value of rewards delivered to self and others when monkeys were free to choose but not when the computer made choices for them. We also found that focal infusion of OT unilaterally into BLA weakly but significantly increased both the frequency of prosocial decisions and attention to recipients for context-specific prosocial decisions, endorsing the hypothesis that OT regulates social behavior, in part, via amygdala neuromodulation. Our findings demonstrate both neurophysiological and neuroendocrinological connections between primate amygdala and social decisions.

Status and the brain.

Social hierarchy is a fact of life for many animals. Navigating social hierarchy requires understanding one's own status relative to others and behaving accordingly, while achieving higher status may call upon cunning and strategic thinking. The neural mechanisms mediating social status have become increasingly well understood in invertebrates and model organisms like fish and mice but until recently have remained more opaque in humans and other primates. In a new study in this issue, Noonan and colleagues explore the neural correlates of social rank in macaques. Using both structural and functional brain imaging, they found neural changes associated with individual monkeys' social status, including alterations in the amygdala, hypothalamus, and brainstem--areas previously implicated in dominance-related behavior in other vertebrates. A separate but related network in the temporal and prefrontal cortex appears to mediate more cognitive aspects of strategic social behavior. These findings begin to delineate the neural circuits that enable us to navigate our own social worlds. A major remaining challenge is identifying how these networks contribute functionally to our social lives, which may open new avenues for developing innovative treatments for social disorders.

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.

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 component analysis (ICA). We estimated voxel-wise 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.

Precuneus is a functional core of the default-mode network.

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.