Smartphones and the Neuroscience of Mental Health.

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal data sets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness.

Opportunity cost determines free-operant action initiation latency and predicts apathy.

BACKGROUND: Apathy, a disabling and poorly understood neuropsychiatric symptom, is characterised by impaired self-initiated behaviour. It has been hypothesised that the opportunity cost of time (OCT) may be a key computational variable linking self-initiated behaviour with motivational status. OCT represents the amount of reward which is foregone per second if no action is taken. Using a novel behavioural task and computational modelling, we investigated the relationship between OCT, self-initiation and apathy. We predicted that higher OCT would engender shorter action latencies, and that individuals with greater sensitivity to OCT would have higher behavioural apathy. METHODS: We modulated the OCT in a novel task called the 'Fisherman Game', Participants freely chose when to self-initiate actions to either collect rewards, or on occasion, to complete non-rewarding actions. We measured the relationship between action latencies, OCT and apathy for each participant across two independent non-clinical studies, one under laboratory conditions (n = 21) and one online (n = 90). 'Average-reward' reinforcement learning was used to model our data. We replicated our findings across both studies. RESULTS: We show that the latency of self-initiation is driven by changes in the OCT. Furthermore, we demonstrate, for the first time, that participants with higher apathy showed greater sensitivity to changes in OCT in younger adults. Our model shows that apathetic individuals experienced greatest change in subjective OCT during our task as a consequence of being more sensitive to rewards. CONCLUSIONS: Our results suggest that OCT is an important variable for determining free-operant action initiation and understanding apathy.

Imbalanced basal ganglia connectivity is associated with motor deficits and apathy in Huntington's disease.

The gating of movement depends on activity within the cortico-striato-thalamic loops. Within these loops, emerging from the cells of the striatum, run two opponent pathways-the direct and indirect basal ganglia pathways. Both are complex and polysynaptic, but the overall effect of activity within these pathways is thought to encourage and inhibit movement, respectively. In Huntington's disease, the preferential early loss of striatal neurons forming the indirect pathway is thought to lead to disinhibition, giving rise to the characteristic motor features of the condition. But early Huntington's disease is also associated with apathy, a loss of motivation and failure to engage in goal-directed movement. We hypothesized that in Huntington's disease, motor signs and apathy may be selectively correlated with indirect and direct pathway dysfunction, respectively. We used spectral dynamic casual modelling of resting-state functional MRI data to model effective connectivity in a model of these cortico-striatal pathways. We tested both of these hypotheses in vivo for the first time in a large cohort of patients with prodromal Huntington's disease. Using an advanced approach at the group level we combined parametric empirical Bayes and Bayesian model reduction procedures to generate a large number of competing models and compare them using Bayesian model comparison. With this automated Bayesian approach, associations between clinical measures and connectivity parameters emerge de novo from the data. We found very strong evidence (posterior probability > 0.99) to support both of our hypotheses. First, more severe motor signs in Huntington's disease were associated with altered connectivity in the indirect pathway components of our model and, by comparison, loss of goal-direct behaviour or apathy, was associated with changes in the direct pathway component. The empirical evidence we provide here demonstrates that imbalanced basal ganglia connectivity may play an important role in the pathogenesis of some of commonest and disabling features of Huntington's disease and may have important implications for therapeutics.

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder.

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks.

A Neurocomputational Model for Intrinsic Reward.

Standard economic indicators provide an incomplete picture of what we value both as individuals and as a society. Furthermore, canonical macroeconomic measures, such as GDP, do not account for non-market activities (e.g., cooking, childcare) that nevertheless impact well-being. Here, we introduce a computational tool that measures the affective value of experiences (e.g., playing a musical instrument without errors). We go on to validate this tool with neural data, using fMRI to measure neural activity in male and female human subjects performing a reinforcement learning task that incorporated periodic ratings of subjective affective state. Learning performance determined level of payment (i.e., extrinsic reward). Crucially, the task also incorporated a skilled performance component (i.e., intrinsic reward) which did not influence payment. Both extrinsic and intrinsic rewards influenced affective dynamics, and their relative influence could be captured in our computational model. Individuals for whom intrinsic rewards had a greater influence on affective state than extrinsic rewards had greater ventromedial prefrontal cortex (vmPFC) activity for intrinsic than extrinsic rewards. Thus, we show that computational modeling of affective dynamics can index the subjective value of intrinsic relative to extrinsic rewards, a "computational hedonometer" that reflects both behavior and neural activity that quantifies the affective value of experience.SIGNIFICANCE STATEMENT Traditional economic indicators are increasingly recognized to provide an incomplete picture of what we value as a society. Standard economic approaches struggle to accurately assign values to non-market activities that nevertheless may be intrinsically rewarding, prompting a need for new tools to measure what really matters to individuals. Using a combination of neuroimaging and computational modeling, we show that despite their lack of instrumental value, intrinsic rewards influence subjective affective state and ventromedial prefrontal cortex (vmPFC) activity. The relative degree to which extrinsic and intrinsic rewards influence affective state is predictive of their relative impacts on neural activity, confirming the utility of our approach for measuring the affective value of experiences and other non-market activities in individuals.

Endogenous fluctuations in the dopaminergic midbrain drive behavioral choice variability.

Human behavior is surprisingly variable, even when facing the same problem under identical circumstances. A prominent example is risky decision making. Economic theories struggle to explain why humans are so inconsistent. Resting-state studies suggest that ongoing endogenous fluctuations in brain activity can influence low-level perceptual and motor processes, but it remains unknown whether endogenous fluctuations also influence high-level cognitive processes including decision making. Here, using real-time functional magnetic resonance imaging, we tested whether risky decision making is influenced by endogenous fluctuations in blood oxygenation level-dependent (BOLD) activity in the dopaminergic midbrain, encompassing ventral tegmental area and substantia nigra. We show that low prestimulus brain activity leads to increased risky choice in humans. Using computational modeling, we show that increased risk taking is explained by enhanced phasic responses to offers in a decision network. Our findings demonstrate that endogenous brain activity provides a physiological basis for variability in complex human behavior.

Computing Value from Quality and Quantity in Human Decision-Making.

How organisms learn the value of single stimuli through experience is well described. In many decisions, however, value estimates are computed "on the fly" by combining multiple stimulus attributes. The neural basis of this computation is poorly understood. Here we explore a common scenario in which decision-makers must combine information about quality and quantity to determine the best option. Using fMRI, we examined the neural representation of quality, quantity, and their integration into an integrated subjective value signal in humans of both genders. We found that activity within inferior frontal gyrus (IFG) correlated with offer quality, while activity in the intraparietal sulcus (IPS) specifically correlated with offer quantity. Several brain regions, including the anterior cingulate cortex (ACC), were sensitive to an interaction of quality and quantity. However, the ACC was uniquely activated by quality, quantity, and their interaction, suggesting that this region provides a substrate for flexible computation of value from both quality and quantity. Furthermore, ACC signals across subjects correlated with the strength of quality and quantity signals in IFG and IPS, respectively. ACC tracking of subjective value also correlated with choice predictability. Finally, activity in the ACC was elevated for choice trials, suggesting that ACC provides a nexus for the computation of subjective value in multiattribute decision-making.SIGNIFICANCE STATEMENT Would you prefer three apples or two oranges? Many choices we make each day require us to weigh up the quality and quantity of different outcomes. Using fMRI, we show that option quality is selectively represented in the inferior frontal gyrus, while option quantity correlates with areas of the intraparietal sulcus that have previously been associated with numerical processing. We show that information about the two is integrated into a value signal in the anterior cingulate cortex, and the fidelity of this integration predicts choice predictability. Our results demonstrate how on-the-fly value estimates are computed from multiple attributes in human value-based decision-making.

Correction: Approach-Induced Biases in Human Information Sampling.

[This corrects the article DOI: 10.1371/journal.pbio.2000638.].

Approach-Induced Biases in Human Information Sampling.

Information sampling is often biased towards seeking evidence that confirms one's prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled ("positive evidence approach"), the selection of which information to sample ("sampling the favorite"), and the interaction between information sampling and subsequent choices ("rejecting unsampled options"). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.

Age-dependent Pavlovian biases influence motor decision-making.

Motor decision-making is an essential component of everyday life which requires weighing potential rewards and punishments against the probability of successfully executing an action. To achieve this, humans rely on two key mechanisms; a flexible, instrumental, value-dependent process and a hardwired, Pavlovian, value-independent process. In economic decision-making, age-related decline in risk taking is explained by reduced Pavlovian biases that promote action toward reward. Although healthy ageing has also been associated with decreased risk-taking in motor decision-making, it is currently unknown whether this is a result of changes in Pavlovian biases, instrumental processes or a combination of both. Using a newly established approach-avoidance computational model together with a novel app-based motor decision-making task, we measured sensitivity to reward and punishment when participants (n = 26,532) made a 'go/no-go' motor gamble based on their perceived ability to execute a complex action. We show that motor decision-making can be better explained by a model with both instrumental and Pavlovian parameters, and reveal age-related changes across punishment- and reward-based instrumental and Pavlovian processes. However, the most striking effect of ageing was a decrease in Pavlovian attraction towards rewards, which was associated with a reduction in optimality of choice behaviour. In a subset of participants who also played an independent economic decision-making task (n = 17,220), we found similar decision-making tendencies for motor and economic domains across a majority of age groups. Pavlovian biases, therefore, play an important role in not only explaining motor decision-making behaviour but also the changes which occur through normal ageing. This provides a deeper understanding of the mechanisms which shape motor decision-making across the lifespan.

Age-related changes in working memory and the ability to ignore distraction.

A weakened ability to effectively resist distraction is a potential basis for reduced working memory capacity (WMC) associated with healthy aging. Exploiting data from 29,631 users of a smartphone game, we show that, as age increases, working memory (WM) performance is compromised more by distractors presented during WM maintenance than distractors presented during encoding. However, with increasing age, the ability to exclude distraction at encoding is a better predictor of WMC in the absence of distraction. A significantly greater contribution of distractor filtering at encoding represents a potential compensation for reduced WMC in older age.

A computational and neural model of momentary subjective well-being.

The subjective well-being or happiness of individuals is an important metric for societies. Although happiness is influenced by life circumstances and population demographics such as wealth, we know little about how the cumulative influence of daily life events are aggregated into subjective feelings. Using computational modeling, we show that emotional reactivity in the form of momentary happiness in response to outcomes of a probabilistic reward task is explained not by current task earnings, but by the combined influence of recent reward expectations and prediction errors arising from those expectations. The robustness of this account was evident in a large-scale replication involving 18,420 participants. Using functional MRI, we show that the very same influences account for task-dependent striatal activity in a manner akin to the influences underpinning changes in happiness.

Dopaminergic Modulation of Decision Making and Subjective Well-Being.

The neuromodulator dopamine has a well established role in reporting appetitive prediction errors that are widely considered in terms of learning. However, across a wide variety of contexts, both phasic and tonic aspects of dopamine are likely to exert more immediate effects that have been less well characterized. Of particular interest is dopamine's influence on economic risk taking and on subjective well-being, a quantity known to be substantially affected by prediction errors resulting from the outcomes of risky choices. By boosting dopamine levels using levodopa (l-DOPA) as human subjects made economic decisions and repeatedly reported their momentary happiness, we show here an effect on both choices and happiness. Boosting dopamine levels increased the number of risky options chosen in trials involving potential gains but not trials involving potential losses. This effect could be better captured as increased Pavlovian approach in an approach-avoidance decision model than as a change in risk preferences within an established prospect theory model. Boosting dopamine also increased happiness resulting from some rewards. Our findings thus identify specific novel influences of dopamine on decision making and emotion that are distinct from its established role in learning.

The influence of contextual reward statistics on risk preference.

Decision theories mandate that organisms should adjust their behaviour in the light of the contextual reward statistics. We tested this notion using a gambling choice task involving distinct contexts with different reward distributions. The best fitting model of subjects' behaviour indicated that the subjective values of options depended on several factors, including a baseline gambling propensity, a gambling preference dependent on reward amount, and a contextual reward adaptation factor. Combining this behavioural model with simultaneous functional magnetic resonance imaging we probed neural responses in three key regions linked to reward and value, namely ventral tegmental area/substantia nigra (VTA/SN), ventromedial prefrontal cortex (vmPFC) and ventral striatum (VST). We show that activity in the VTA/SN reflected contextual reward statistics to the extent that context affected behaviour, activity in the vmPFC represented a value difference between chosen and unchosen options while VST responses reflected a non-linear mapping between the actual objective rewards and their subjective value. The findings highlight a multifaceted basis for choice behaviour with distinct mappings between components of this behaviour and value sensitive brain regions.

Perimovement decrease of alpha/beta oscillations in the human nucleus accumbens.

The human nucleus accumbens is thought to play an important role in guiding future action selection via an evaluation of current action outcomes. Here we provide electrophysiological evidence for a more direct, i.e., online, role during action preparation. We recorded local field potentials from the nucleus accumbens in patients with epilepsy undergoing surgery for deep brain stimulation. We found a consistent decrease in the power of alpha/beta oscillations (10-30 Hz) before and around the time of movements. This perimovement alpha/beta desynchronization was observed in seven of eight patients and was present both before instructed movements in a serial reaction time task as well as before self-paced, deliberate choices in a decision making task. A similar beta decrease over sensorimotor cortex and in the subthalamic nucleus has been directly related to movement preparation and execution. Our results support the idea of a direct role of the human nucleus accumbens in action preparation and execution.

Phasic dopamine release in the rat nucleus accumbens symmetrically encodes a reward prediction error term.

Making predictions about the rewards associated with environmental stimuli and updating those predictions through feedback is an essential aspect of adaptive behavior. Theorists have argued that dopamine encodes a reward prediction error (RPE) signal that is used in such a reinforcement learning process. Recent work with fMRI has demonstrated that the BOLD signal in dopaminergic target areas meets both necessary and sufficient conditions of an axiomatic model of the RPE hypothesis. However, there has been no direct evidence that dopamine release itself also meets necessary and sufficient criteria for encoding an RPE signal. Further, the fact that dopamine neurons have low tonic firing rates that yield a limited dynamic range for encoding negative RPEs has led to significant debate about whether positive and negative prediction errors are encoded on a similar scale. To address both of these issues, we used fast-scan cyclic voltammetry to measure reward-evoked dopamine release at carbon fiber electrodes chronically implanted in the nucleus accumbens core of rats trained on a probabilistic decision-making task. We demonstrate that dopamine concentrations transmit a bidirectional RPE signal with symmetrical encoding of positive and negative RPEs. Our findings strengthen the case that changes in dopamine concentration alone are sufficient to encode the full range of RPEs necessary for reinforcement learning.

No unified reward prediction error in local field potentials from the human nucleus accumbens: evidence from epilepsy patients.

Functional magnetic resonance imaging (fMRI), cyclic voltammetry, and single-unit electrophysiology studies suggest that signals measured in the nucleus accumbens (Nacc) during value-based decision making represent reward prediction errors (RPEs), the difference between actual and predicted rewards. Here, we studied the precise temporal and spectral pattern of reward-related signals in the human Nacc. We recorded local field potentials (LFPs) from the Nacc of six epilepsy patients during an economic decision-making task. On each trial, patients decided whether to accept or reject a gamble with equal probabilities of a monetary gain or loss. The behavior of four patients was consistent with choices being guided by value expectations. Expected value signals before outcome onset were observed in three of those patients, at varying latencies and with nonoverlapping spectral patterns. Signals after outcome onset were correlated with RPE regressors in all subjects. However, further analysis revealed that these signals were better explained as outcome valence rather than RPE signals, with gamble gains and losses differing in the power of beta oscillations and in evoked response amplitudes. Taken together, our results do not support the idea that postsynaptic potentials in the Nacc represent a RPE that unifies outcome magnitude and prior value expectation. We discuss the generalizability of our findings to healthy individuals and the relation of our results to measurements of RPE signals obtained from the Nacc with other methods.

Cortical drive of low-frequency oscillations in the human nucleus accumbens during action selection.

The nucleus accumbens is thought to contribute to action selection by integrating behaviorally relevant information from multiple regions, including prefrontal cortex. Studies in rodents suggest that information flow to the nucleus accumbens may be regulated via task-dependent oscillatory coupling between regions. During instrumental behavior, local field potentials (LFP) in the rat nucleus accumbens and prefrontal cortex are coupled at delta frequencies (Gruber AJ, Hussain RJ, O'Donnell P. PLoS One 4: e5062, 2009), possibly mediating suppression of afferent input from other areas and thereby supporting cortical control (Calhoon GG, O'Donnell P. Neuron 78: 181-190, 2013). In this report, we demonstrate low-frequency cortico-accumbens coupling in humans, both at rest and during a decision-making task. We recorded LFP from the nucleus accumbens in six epilepsy patients who underwent implantation of deep brain stimulation electrodes. All patients showed significant coherence and phase-synchronization between LFP and surface EEG at delta and low theta frequencies. Although the direction of this coupling as indexed by Granger causality varied between subjects in the resting-state data, all patients showed a cortical drive of the nucleus accumbens during action selection in a decision-making task. In three patients this was accompanied by a significant coherence increase over baseline. Our results suggest that low-frequency cortico-accumbens coupling represents a highly conserved regulatory mechanism for action selection.

Dorsal striatum is necessary for stimulus-value but not action-value learning in humans.

Several lines of evidence implicate the striatum in learning from experience on the basis of positive and negative feedback. However, the necessity of the striatum for such learning has been difficult to demonstrate in humans, because brain damage is rarely restricted to this structure. Here we test a rare individual with widespread bilateral damage restricted to the dorsal striatum. His performance was impaired and not significantly different from chance on several classic learning tasks, consistent with current theories regarding the role of the striatum. However, he also exhibited remarkably intact performance on a different subset of learning paradigms. The tasks he could perform can all be solved by learning the value of actions, while those he could not perform can only be solved by learning the value of stimuli. Although dorsal striatum is often thought to play a specific role in action-value learning, we find surprisingly that dorsal striatum is necessary for stimulus-value but not action-value learning in humans.

Using games to understand the mind.

Board, card or video games have been played by virtually every individual in the world. Games are popular because they are intuitive and fun. These distinctive qualities of games also make them ideal for studying the mind. By being intuitive, games provide a unique vantage point for understanding the inductive biases that support behaviour in more complex, ecological settings than traditional laboratory experiments. By being fun, games allow researchers to study new questions in cognition such as the meaning of 'play' and intrinsic motivation, while also supporting more extensive and diverse data collection by attracting many more participants. We describe the advantages and drawbacks of using games relative to standard laboratory-based experiments and lay out a set of recommendations on how to gain the most from using games to study cognition. We hope this Perspective will lead to a wider use of games as experimental paradigms, elevating the ecological validity, scale and robustness of research on the mind.