Miriam C. Klein-Flügge

Oxidative Phosphorylation, Not Glycolysis, Powers Presynaptic and Postsynaptic Mechanisms Underlying Brain Information Processing

Neural activity has been suggested to initially trigger ATP production by glycolysis, rather than oxidative phosphorylation, for three reasons: glycolytic enzymes are associated with ion pumps; neurons may increase their energy supply by activating glycolysis in astrocytes to generate lactate; and activity increases glucose uptake more than O2 uptake. In rat hippocampal slices, neuronal activity rapidly decreased the levels of extracellular O2 and intracellular NADH (reduced nicotinamide adenine dinucleotide), even with lactate dehydrogenase blocked to prevent lactate generation, or with only 20% superfused O2 to mimic physiological O2 levels. Pharmacological analysis revealed an energy budget in which 11% of O2 use was on presynaptic action potentials, 17% was on presynaptic Ca2+ entry and transmitter release, 46% was on postsynaptic glutamate receptors, and 26% was on postsynaptic action potentials, in approximate accord with theoretical brain energy budgets. Thus, the major mechanisms mediating brain information processing are all initially powered by oxidative phosphorylation, and an astrocyte–neuron lactate shuttle is not needed for this to occur.

An agent independent axis for executed and modeled choice in medial prefrontal cortex.

Summary Adaptive success in social animals depends on an ability to infer the likely actions of others. Little is known about the neural computations that underlie this capacity. Here, we show that the brain models the values and choices of others even when these values are currently irrelevant. These modeled choices use the same computations that underlie our own choices, but are resolved in a distinct neighboring medial prefrontal brain region. Crucially, however, when subjects choose on behalf of a partner instead of themselves, these regions exchange their functional roles. Hence, regions that represented values of the subject’s executed choices now represent the values of choices executed on behalf of the partner, and those that previously modeled the partner now model the subject. These data tie together neural computations underlying self-referential and social inference, and in so doing establish a new functional axis characterizing the medial wall of prefrontal cortex.

Segregated encoding of reward-identity and stimulus-reward associations in human orbitofrontal cortex.

A dominant focus in studies of learning and decision-making is the neural coding of scalar reward value. This emphasis ignores the fact that choices are strongly shaped by a rich representation of potential rewards. Here, using fMRI adaptation, we demonstrate that responses in the human orbitofrontal cortex (OFC) encode a representation of the specific type of food reward predicted by a visual cue. By controlling for value across rewards and by linking each reward with two distinct stimuli, we could test for representations of reward–identity that were independent of associative information. Our results show reward–identity representations in a medial-caudal region of OFC, independent of the associated predictive stimulus. This contrasts with a more rostro-lateral OFC region encoding reward–identity representations tied to the predicate stimulus. This demonstration of adaptation in OFC to reward specific representations opens an avenue for investigation of more complex decision mechanisms that are not immediately accessible in standard analyses, which focus on correlates of average activity.

Time-dependent changes in human corticospinal excitability reveal value-based competition for action during decision processing.

Our choices often require appropriate actions to obtain a preferred outcome, but the neural underpinnings that link decision making and action selection remain largely undetermined. Recent theories propose that action selection occurs simultaneously, i.e., parallel in time, with the decision process. Specifically, it is thought that action selection in motor regions originates from a competitive process that is gradually biased by evidence signals originating in other regions, such as those specialized in value computations. Biases reflecting the evaluation of choice options should thus emerge in the motor system before the decision process is complete. Using transcranial magnetic stimulation, we sought direct physiological evidence for this prediction by measuring changes in corticospinal excitability in human motor cortex during value-based decisions. We found that excitability for chosen versus unchosen actions distinguishes the forthcoming choice before completion of the decision process. Both excitability and reaction times varied as a function of the subjective value-difference between chosen and unchosen actions, consistent with this effect being value-driven. This relationship was not observed in the absence of a decision. Our data provide novel evidence in humans that internally generated value-based decisions influence the competition between action representations in motor cortex before the decision process is complete. This is incompatible with models of serial processing of stimulus, decision, and action.

Dissociable reward and timing signals in human midbrain and ventral striatum.

Summary Reward prediction error (RPE) signals are central to current models of reward-learning. Temporal difference (TD) learning models posit that these signals should be modulated by predictions, not only of magnitude but also timing of reward. Here we show that BOLD activity in the VTA conforms to such TD predictions: responses to unexpected rewards are modulated by a temporal hazard function and activity between a predictive stimulus and reward is depressed in proportion to predicted reward. By contrast, BOLD activity in ventral striatum (VS) does not reflect a TD RPE, but instead encodes a signal on the variable relevant for behavior, here timing but not magnitude of reward. The results have important implications for dopaminergic models of cortico-striatal learning and suggest a modification of the conventional view that VS BOLD necessarily reflects inputs from dopaminergic VTA neurons signaling an RPE.

Behavioral Modeling of Human Choices Reveals Dissociable Effects of Physical Effort and Temporal Delay on Reward Devaluation

There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.

Variability of Human Corticospinal Excitability Tracks the State of Action Preparation

Task-evoked trial-by-trial variability is a ubiquitous property of neural responses, yet its functional role remains largely unclear. Recent work in nonhuman primates shows that the temporal structure of neural variability in several brain regions is task-related. For example, trial-by-trial variability in premotor cortex tracks motor preparation with increasingly consistent firing rates and thus a decline in variability before movement onset. However, whether noninvasive measures of the variability of population activity available from humans can similarly track the preparation of actions remains unknown. We tested this by using single-pulse transcranial magnetic stimulation (TMS) over primary motor cortex (M1) to measure corticospinal excitability (CSE) at different times during action preparation. First, we established the basic properties of intrinsic CSE variability at rest. Then, during the task, responses (left or right button presses) were either directly instructed (forced choice) or resulted from a value decision (choice). Before movement onset, we observed a temporally specific task-related decline in CSE variability contralateral to the responding hand. This decline was stronger in fast-response compared with slow-response trials, consistent with data in nonhuman primates. For the nonresponding hand, CSE variability also decreased, but only in choice trials, and earlier compared with the responding hand, possibly reflecting choice-specific suppression of unselected actions. These findings suggest that human CSE variability measured by TMS over M1 tracks the state of motor preparation, and may reflect the optimization of preparatory population activity. This provides novel avenues in humans to assess the dynamics of action preparation but also more complex processes, such as choice-to-action transformations.

Selective alteration of human value decisions with medial frontal tDCS is predicted by changes in attractor dynamics

During value-based decision making, ventromedial prefrontal cortex (vmPFC) is thought to support choices by tracking the expected gain from different outcomes via a competition-based process. Using a computational neurostimulation approach we asked how perturbing this region might alter this competition and resulting value decisions. We simulated a perturbation of neural dynamics in a biophysically informed model of decision-making through in silico depolarization at the level of neuronal ensembles. Simulated depolarization increased baseline firing rates of pyramidal neurons, which altered their susceptibility to background noise, and thereby increased choice stochasticity. These behavioural predictions were compared to choice behaviour in healthy participants performing similar value decisions during transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique. We placed the soma depolarizing electrode over medial frontal PFC. In line with model predictions, this intervention resulted in more random choices. By contrast, no such effect was observed when placing the depolarizing electrode over lateral PFC. Using a causal manipulation of ventromedial and lateral prefrontal function, these results provide support for competition-based choice dynamics in human vmPFC, and introduce computational neurostimulation as a mechanistic assay for neurostimulation studies of cognition.

Understanding psychiatric disorder by capturing ecologically relevant features of learning and decision-making

HighlightsTasks incorporating ecological features provide insights into learning and decision‐making.Distinct neural processes are recruited depending on the precise nature of the task.Computational modelling can help dissect component processes in complex scenarios.Psychiatric research may benefit from combining modelling with ecological tasks. Abstract Recent research in cognitive neuroscience has begun to uncover the processes underlying increasingly complex voluntary behaviours, including learning and decision‐making. Partly this success has been possible by progressing from simple experimental tasks to paradigms that incorporate more ecological features. More specifically, the premise is that to understand cognitions and brain functions relevant for real life, we need to introduce some of the ecological challenges that we have evolved to solve. This often entails an increase in task complexity, which can be managed by using computational models to help parse complex behaviours into specific component mechanisms. Here we propose that using computational models with tasks that capture ecologically relevant learning and decision‐making processes may provide a critical advantage for capturing the mechanisms underlying symptoms of disorders in psychiatry. As a result, it may help develop mechanistic approaches towards diagnosis and treatment. We begin this review by mapping out the basic concepts and models of learning and decision‐making. We then move on to consider specific challenges that emerge in realistic environments and describe how they can be captured by tasks. These include changes of context, uncertainty, reflexive/emotional biases, cost‐benefit decision‐making, and balancing exploration and exploitation. Where appropriate we highlight future or current links to psychiatry. We particularly draw examples from research on clinical depression, a disorder that greatly compromises motivated behaviours in real‐life, but where simpler paradigms have yielded mixed results. Finally, we highlight several paradigms that could be used to help provide new insights into the mechanisms of psychiatric disorders.

Offline impact of transcranial focused ultrasound on cortical activation in primates

To understand brain circuits it is necessary both to record and manipulate their activity. Transcranial ultrasound (TUS) is a promising non-invasive brain stimulation technique. To date, investigations have focused on short-lived neuromodulatory effects, but to deliver on its full potential for research and therapy, ultrasound protocols are required that induce longer-lasting ‘offline’ changes. Here, we present a TUS protocol that modulates brain activation in macaques for more than one hour after 40 seconds of stimulation, while circumventing auditory confounds. Normally activity in brain areas reflects activity in interconnected regions but TUS’ impact can be demonstrated by showing such patterns change for stimulated areas. We report regionally specific TUS effects for two medial frontal brain regions – supplementary motor area and frontal polar cortex. Independently of these site-specific effects, TUS also induced signal changes in the meningeal compartment. TUS effects were temporary and not associated with microstructural changes.