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When we make decisions, the benefits of an option often need to be weighed against accompanying costs. Little is known, however, about the neural systems underlying such cost-benefit computations. Using functional magnetic resonance imaging and choice modeling, we show that decision making based on cost-benefit comparison can be explained as a stochastic accumulation of costbenefit difference. Model-driven functional MRI shows that ventromedial and left dorsolateral prefrontal cortex compare costs and benefits by computing the difference between neural signatures of anticipated benefits and costs from the ventral striatum and amygdala, respectively. Moreover, changes in blood oxygen level dependent (BOLD) signal in the bilateral middle intraparietal sulcus reflect the accumulation of the difference signal from ventromedial prefrontal cortex. In sum, we show that a neurophysiological mechanism previously established for perceptual decision making, that is, the difference-based accumulation of evidence, is fundamental also in value-based decisions. The brain, thus, weighs costs against benefits by combining neural benefit and cost signals into a single, differencebased neural representation of net value, which is accumulated over time until the individual decides to accept or reject an option.hen we make decisions, the benefits of a decision option often need to be weighed against accompanying costs. Cost-benefit integration, thus, is an important aspect of decision making. However, value-based decision making is typically investigated in the context of decision uncertainty (1-3), so little is known about the neural mechanisms underlying the integration of costs and benefits as such.Cost-benefit-based decision making involves the binary decision to either accept or reject a choice option based on two competing attributes-the option's expected rewards and losses. Such binary accept-versus-reject decisions bear a strong resemblance to twoalternative choices in perceptual decision making (4, 5). For example, when monkeys performed a direction-of-motion discrimination task in which they had to decide whether a noisy field of dots was moving in one direction or its opposite direction (e.g., leftward or rightward) and indicated their choice with a quick eye movement to the target on the respective side, motion-sensitive neurons in middle temporal visual area MT either respond to leftward motion or to rightward motion. Prefrontal and parietal neurons, in contrast, form a decision by accumulating the difference in the activities of populations of neurons in area MT that code for opposite directions of motion. The monkey's saccade response is faster when more dots are moving in one direction, and this effect is predicted by the strength of the accumulated neuronal difference signal (6). A difference-based decision mechanism has also been identified in the human dorsolateral prefrontal cortex (DLPFC) during perceptual face-house decisions (4, 7). Thus, we hypothesized that cost-benefit decisions involve an analogous decisio...

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Recent studies suggest an association of dopamine D2 receptor (DRD2) availability with flexibility in reward-based learning. We extend these results by demonstrating an association of genetically based differences in DRD2 density with the ability to intentionally switch between nonrewarded tasks: noncarriers of the A1 allele of the DRD2/ANKK1-TaqIa polymorphism, associated with higher DRD2 density, show increased task-switching costs, increased prefrontal switching activity in the inferior frontal junction area, and increased functional connectivity in dorsal frontostriatal circuits, relative to A1 allele carriers. A DRD2 haplotype analysis in the same sample confirmed these results, indicating an association between high D2 density and increased task-switching effort. Our results provide evidence that converges with that from association studies relating increased D2 density to deficits in cognitive flexibility in schizophrenia. We suggest that individual differences in striatal D2 signaling in healthy humans modulate goal-directed gating to prefrontal cortex, thus leading to individual differences in switching intentionally to newly relevant behaviors.

Abstract■ An impairment of attentional control in the face of threat-related distracters is well established for high-anxious individuals. Beyond that, it has been hypothesized that high trait anxiety more generally impairs the neural efficiency of cognitive processes requiring attentional control-even in the absence of threat-related stimuli. Here, we use fMRI to show that trait anxiety indeed modulates brain activation and functional connectivities between task-relevant brain regions in an affectively neutral Stroop task. In high-anxious individuals, dorsolateral pFC showed stronger task-related activation and reduced coupling with posterior lateral frontal regions, dorsal ACC, and a word-sensitive area in the left fusiform gyrus. These results support the assumption that a general (i.e., not threat-specific) impairment of attentional control leads to reduced neural processing efficiency in anxious individuals. The increased dorsolateral pFC activation is interpreted as an attempt to compensate for suboptimal connectivity within the cortical network subserving task performance. ■

Abstract■ The pFC is critical for cognitive flexibility (i.e., our ability to flexibly adjust behavior to changing environmental demands), but also for cognitive stability (i.e., our ability to follow behavioral plans in the face of distraction). Behavioral research suggests that individuals differ in their cognitive flexibility and stability, and neurocomputational theories of working memory relate this variability to the concept of attractor stability in recurrently connected neural networks. We introduce a novel task paradigm to simultaneously assess flexible switching between task rules (cognitive flexibility) and task performance in the presence of irrelevant distractors (cognitive stability) and to furthermore assess the individual "spontaneous switching rate" in response to ambiguous stimuli to quantify the individual dispositional cognitive flexibility in a theoretically motivated way (i.e., as a proxy for attractor stabi-

General intelligence is a psychological construct that captures in a single metric the overall level of behavioural and cognitive performance in an individual. While previous research has attempted to localise intelligence in circumscribed brain regions, more recent work focuses on functional interactions between regions. However, even though brain networks are characterised by substantial modularity, it is unclear whether and how the brain’s modular organisation is associated with general intelligence. Modelling subject-specific brain network graphs from functional MRI resting-state data (N = 309), we found that intelligence was not associated with global modularity features (e.g., number or size of modules) or the whole-brain proportions of different node types (e.g., connector hubs or provincial hubs). In contrast, we observed characteristic associations between intelligence and node-specific measures of within- and between-module connectivity, particularly in frontal and parietal brain regions that have previously been linked to intelligence. We propose that the connectivity profile of these regions may shape intelligence-relevant aspects of information processing. Our data demonstrate that not only region-specific differences in brain structure and function, but also the network-topological embedding of fronto-parietal as well as other cortical and subcortical brain regions is related to individual differences in higher cognitive abilities, i.e., intelligence.

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