Neural Signatures of Economic Parameters During Decision-Making: A Functional MRI (fMRI), Electroencephalography (EEG) and Autonomic Monitoring Study

Abstract

Adaptive behaviour requires an ability to obtain rewards by choosing between different risky options. Financial gambles can be used to study effective decision-making experimentally, and to distinguish processes involved in choice option evaluation from outcome feedback and other contextual factors. Here, we used a paradigm where participants evaluated 'mixed' gambles, each presenting a potential gain and a potential loss and an associated variable outcome probability. We recorded neural responses using autonomic monitoring, electroencephalography (EEG) and functional neuroimaging (fMRI), and used a univariate, parametric design to test for correlations with the eleven economic parameters that varied across gambles, including expected value (EV) and amount magnitude. Consistent with behavioural economic theory, participants were risk-averse. Gamble evaluation generated detectable autonomic responses, but only weak correlations with outcome uncertainty were found, suggesting that peripheral autonomic feedback does not play a major role in this task. Long-latency stimulus-evoked EEG potentials were sensitive to expected gain and expected value, while alpha-band power reflected expected loss and amount magnitude, suggesting parallel representations of distinct economic qualities in cortical activation and central arousal. Neural correlates of expected value representation were localized using fMRI to ventromedial prefrontal cortex, while the processing of other economic parameters was associated with distinct patterns across lateral prefrontal, cingulate, insula and occipital cortices including default-mode network and early visual areas. These multimodal data provide complementary evidence for distributed substrates of choice evaluation across multiple, predominantly cortical, brain systems wherein distinct regions are preferentially attuned to specific economic features. Our findings extend biologically-plausible models of risky decision-making while providing potential biomarkers of economic representations that can be applied to the study of deficits in motivational behaviour in neurological and psychiatric patients.

Quantifying the time for accurate EEG decoding of single value-based decisions

“…The vast majority of M/EEG studies in value-based decision making focused on the period after subjects receive feedback about their decisions (for example (Cohen and Ranganath, 2007;Frank et al, 2005;Pedroni et al, 2011;Yeung et al, 2005) and examine the effect of this feedback on subsequent behavior. Only few studies have assessed the temporal dynamics of the value encoding phase in humans, right after an offer presentation (Gluth et al, 2013;Hunt et al, 2012;Minati et al, 2012) but these were either based on ERP responses at single electrodes (Gluth et al, 2013;Minati et al, 2012), or were restricted within specific brain regions (Hunt et al, 2012). Our study instead examines global measures of EEG activity and provides further insight into the processes taking place while subjects see an offer and encode its value by quantifying the time of the single decision for each subject.…”

A mini review of functional magnetic resonance imaging

“…However, fMRI has a limited time resolution, is highly sensitive to motion artifact and can only provide an indirect measurement of neural activity. An increasing number of studies have focused on the multimodal brain mapping techniques for the research in neuroscience or physiopathology [36][37][38][39]. The other well-known functional imaging techniques include electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET) and functional near infrared spectroscopy (fNIRS).…”

Advances in Physiological Computing

“…fMRI studies have shown that blood oxygen signals in the prefrontal cortex develop during economic choices where users must make gambling evaluations and expected value decisions (Minati et al 2012) and when determining decision value for different categories of goods for purchase (Grabenhorst and Rolls 2011). Using these activation patterns, Peck et al (2013a) demonstrated the use of fNIRS as input to information-filtering systems, constructing a movie recommendation engine that gradually personalized movies to the user based on predicted preference values.…”