Rafael Polania

Neural Oscillations and Synchronization Differentially Support Evidence Accumulation in Perceptual and Value-Based Decision Making

Organisms make two types of decisions on a regular basis. Perceptual decisions are determined by objective states of the world (e.g., melons are bigger than apples), whereas value-based decisions are determined by subjective preferences (e.g., I prefer apples to melons). Theoretical accounts suggest that both types of choice involve neural computations accumulating evidence for the choice alternatives; however, little is known about the overlap or differences in the processes underlying perceptual versus value-based decisions. We analyzed EEG recordings during a paradigm where perceptual- and value-based choices were based on identical stimuli. For both types of choice, evidence accumulation was evident in parietal gamma-frequency oscillations, whereas a similar frontal signal was unique for value-based decisions. Fronto-parietal synchronization of these signals predicted value-based choice accuracy. These findings uncover how decisions emerge from topographic- and frequency-specific oscillations that accumulate distinct aspects of evidence, with large-scale synchronization as a mechanism integrating these spatially distributed signals.

The precision of value-based choices depends causally on fronto-parietal phase coupling

Which meal would you like today, chicken or pasta? For such value-based choices, organisms must flexibly integrate various types of sensory information about internal states and the environment to transform them into actions. Recent accounts suggest that these choice-relevant processes are mediated by information transfer between functionally specialized but spatially distributed brain regions in parietal and prefrontal cortex; however, it remains unclear whether such fronto-parietal communication is causally involved in guiding value-based choices. We find that transcranially inducing oscillatory desynchronization between the frontopolar and -parietal cortex leads to more inaccurate choices between food rewards while leaving closely matched perceptual decisions unaffected. Computational modelling shows that this exogenous manipulation leads to imprecise value assignments to the choice alternatives. Thus, our study demonstrates that accurate value-based decisions critically involve coherent rhythmic information transfer between fronto-parietal brain areas and establishes an experimental approach to non-invasively manipulate the precision of value-based choices in humans.

Automatic versus Choice-Dependent Value Representations in the Human Brain

The subjective values of choice options can impact on behavior in two fundamentally different types of situations: first, when people explicitly base their actions on such values, and second, when values attract attention despite being irrelevant for current behavior. Here we show with functional magnetic resonance imaging (fMRI) that these two behavioral functions of values are encoded in distinct regions of the human brain. In the medial prefrontal cortex, value-related activity is enhanced when subjective value becomes choice-relevant, and the magnitude of this increase relates directly to the outcome and reliability of the value-based choice. In contrast, activity in the posterior cingulate cortex represents values similarly when they are relevant or irrelevant for the present choice, and the strength of this representation predicts attentional capture by choice-irrelevant values. Our results suggest that distinct components of the brains valuation network encode value in context-dependent manners that serve fundamentally different behavioral aims.

Studying and modifying brain function with non-invasive brain stimulation

In the past three decades, our understanding of brain–behavior relationships has been significantly shaped by research using non-invasive brain stimulation (NIBS) techniques. These methods allow non-invasive and safe modulation of neural processes in the healthy brain, enabling researchers to directly study how experimentally altered neural activity causally affects behavior. This unique property of NIBS methods has, on the one hand, led to groundbreaking findings on the brain basis of various aspects of behavior and has raised interest in possible clinical and practical applications of these methods. On the other hand, it has also triggered increasingly critical debates about the properties and possible limitations of these methods. In this review, we discuss these issues, clarify the challenges associated with the use of currently available NIBS techniques for basic research and practical applications, and provide recommendations for studies using NIBS techniques to establish brain–behavior relationships.Polanía, Nitsche and Ruff summarize the state of non-invasive brain stimulation research in humans, discuss some current debates about properties and limitations of these methods, and give recommendations for how these challenges may be addressed.

Transcranial Stimulation over Frontopolar Cortex Elucidates the Choice Attributes and Neural Mechanisms Used to Resolve Exploration–Exploitation Trade-Offs

Optimal behavior requires striking a balance between exploiting tried-and-true options or exploring new possibilities. Neuroimaging studies have identified different brain regions in humans where neural activity is correlated with exploratory or exploitative behavior, but it is unclear whether this activity directly implements these choices or simply reflects a byproduct of the behavior. Moreover, it remains unknown whether arbitrating between exploration and exploitation can be influenced with exogenous methods, such as brain stimulation. In our study, we addressed these questions by selectively upregulating and downregulating neuronal excitability with anodal or cathodal transcranial direct current stimulation over right frontopolar cortex during a reward-learning task. This caused participants to make slower, more exploratory or faster, more exploitative decisions, respectively. Bayesian computational modeling revealed that stimulation affected how much participants took both expected and obtained rewards into account when choosing to exploit or explore: Cathodal stimulation resulted in an increased focus on the option expected to yield the highest payout, whereas anodal stimulation led to choices that were less influenced by anticipated payoff magnitudes and were more driven by recent negative reward prediction errors. These findings suggest that exploration is triggered by a neural mechanism that is sensitive to prior less-than-expected choice outcomes and thus pushes people to seek out alternative courses of action. Together, our findings establish a parsimonious neurobiological mechanism that causes exploration and exploitation, and they provide new insights into the choice features used by this mechanism to direct decision-making. SIGNIFICANCE STATEMENT We often must choose whether to try something new (exploration) or stick with a proven strategy (exploitation). Balancing this trade-off is important for survival and growth across species because, without exploration, we would perseverate with the same strategies and never discover better options. Which brain mechanisms are responsible for our ability to make these decisions? We show that applying different types of noninvasive brain stimulation over frontopolar cortex causes participants to explore more or less in uncertain environments. These changes in exploration reflect how much participants focus on expected payoffs and on memory of recent disappointments. Thus, our results characterize a neural mechanism that systematically incorporates anticipated rewards and past experiences to trigger exploration of alternative courses of action.

A causal account of the brain network computations underlying strategic social behavior

During competitive interactions, humans have to estimate the impact of their own actions on their opponents strategy. Here we provide evidence that neural computations in the right temporoparietal junction (rTPJ) and interconnected structures are causally involved in this process. By combining inhibitory continuous theta-burst transcranial magnetic stimulation with model-based functional MRI, we show that disrupting neural excitability in the rTPJ reduces behavioral and neural indices of mentalizing-related computations, as well as functional connectivity of the rTPJ with ventral and dorsal parts of the medial prefrontal cortex. These results provide a causal demonstration that neural computations instantiated in the rTPJ are neurobiological prerequisites for the ability to integrate opponent beliefs into strategic choice, through system-level interaction within the valuation and mentalizing networks.

Brain Network Mechanisms Underlying Motor Enhancement by Transcranial Entrainment of Gamma Oscillations.

Gamma and beta oscillations are routinely observed in motor-related brain circuits during movement preparation and execution. Entrainment of gamma or beta oscillations via transcranial alternating current stimulation (tACS) over primary motor cortex (M1) has opposite effects on motor performance, suggesting a causal role of these brain rhythms for motor control. However, it is largely unknown which brain mechanisms characterize these changes in motor performance brought about by tACS. In particular, it is unclear whether these effects result from brain activity changes only in the targeted areas or within functionally connected brain circuits. Here we investigated this issue by applying gamma-band and beta-band tACS over M1 in healthy humans during a visuomotor task and concurrent functional magnetic resonance imaging (fMRI). Gamma tACS indeed improved both the velocity and acceleration of visually triggered movements, compared with both beta tACS and sham stimulation. Beta tACS induced a numerical decrease in velocity compared with sham stimulation, but this was not statistically significant. Crucially, gamma tACS induced motor performance enhancements correlated with changed BOLD activity in the stimulated M1. Moreover, we found frequency- and task-specific neural compensatory activity modulations in the dorsomedial prefrontal cortex (dmPFC), suggesting a key regulatory role of this region in motor performance. Connectivity analyses revealed that the dmPFC interacted functionally with M1 and with regions within the executive motor system. These results suggest a role of the dmPFC for motor control and show that tACS-induced behavioral changes not only result from activity modulations underneath the stimulation electrode but also reflect compensatory modulation within connected and functionally related brain networks. More generally, our results illustrate how combined tACS–fMRI can be used to resolve the causal link between cortical rhythms, brain systems, and behavior. SIGNIFICANCE STATEMENT Recent research has suggested a causal role for gamma oscillations during movement preparation and execution. Here we combine transcranial alternating current stimulation (tACS) with functional magnetic resonance imaging (fMRI) to identify the neural mechanisms that accompany motor performance enhancements triggered by gamma tACS over the primary motor cortex. We show that the tACS-induced motor performance enhancements correlate with changed neural activity in the stimulated area and modulate, in a frequency- and task-specific manner, the neural activity in the dorsomedial prefrontal cortex. This suggests a regulatory role of this region for motor control. More generally, we show that combined tACS–fMRI can elucidate the causal link between brain oscillations, neural systems, and behavior.

Concurrent tACS-fMRI Reveals Causal Influence of Power Synchronized Neural Activity on Resting State fMRI Connectivity

Resting state fMRI (rs-fMRI) is commonly used to study the brains intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSNs). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices. The two driving tACS signals were tailored to the individuals α rhythm (8–12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either α oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS). Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared with phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brains intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity. SIGNIFICANCE STATEMENT Resting state fMRI (rs-fMRI) has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes. It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power synchronization mechanisms. These novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology, and Psychology.

Dissociable mechanisms govern when and how strongly reward attributes affect decisions

Theories and computational models of decision making usually focus on how strongly different attributes are weighted in choice, e.g., as a function of their importance or salience to the decision-maker. However, when different attributes impact on the decision process is a question that has received far less attention. Here, we investigated whether attribute consideration timing has a unique influence on decision making using a time-varying drift diffusion model and data from four separate experiments. Experimental manipulations of attention and neural activity demonstrated that we can dissociate the processes that determine the relative weighting strength and timing of attribute consideration. Thus, the processes determining either the weighting strengths or the timing of attributes in decision making can adapt independently to changes in the environment or goals. Quantifying these separate influences of timing and weighting on choice improves our understanding and predictions of individual differences in decision behaviour.

Efficient coding of subjective value

Preference-based decisions are essential for survival, for instance when deciding what we should (not) eat. Despite their importance, choices based on preferences are surprisingly variable and can appear irrational in ways that have defied mechanistic explanations. Here we propose that subjective valuation results from an inference process that accounts for the information structure of values in the environment and that maximizes information in value representations in line with demands imposed by limited coding resources. A model of this inference process explains the variability in both subjective value reports and preference-based choices, and predicts a new preference illusion that we validate with empirical data. Interestingly, the same model also explains the level of confidence associated with these reports. Our results imply that preference-based decisions reflect information-maximizing transmission and statistically optimal decoding of subjective values by a limited-capacity system. These findings provide a unified account of how humans perceive and valuate the environment to optimally guide behavior.