Altered neural activity to monetary reward/loss processing in episodic migraine

The dysfunctions of the mesolimbic cortical reward circuit have been proposed to contribute to migraine pain. Although supporting empirical evidence was mainly found in connection with primary rewards or in chronic migraine where the pain experience is (almost) constant. Our goal however was to investigate the neural correlates of secondary reward/loss anticipation and consumption using the monetary incentive delay task in 29 episodic migraine patients and 41 headache-free controls. Migraine patients showed decreased activation in one cluster covering the right inferior frontal gyrus during reward consumption compared to controls. We also found significant negative correlation between the time of the last migraine attack before the scan and activation of the parahippocampal gyrus and the right hippocampus yielded to loss anticipation. During reward/loss consumption, a relative increase in the activity of the visual areas was observed the more time passed between the last attack and the scan session. Our results suggest intact reward/loss anticipation but altered reward consumption in migraine, indicating a decreased reactivity to monetary rewards. The findings also raise the possibility that neural responses to loss anticipation and reward/loss consumption could be altered by the proximity of the last migraine attack not just during pre-ictal periods, but interictally as well

Information Filtering in Electronic Networks of Practice: An fMRI Investigation of Expectation (Dis)confirmation

Online forums sponsored by electronic networks of practice (ENPs) have become an important platform for technology-mediated knowledge exchange, yet relatively little is known about how ENP participants filter and evaluate the information they encounter on these forums. This study integrates perspectives from expectation confirmation theory, prospect theory, and neuroscience research to explore how ENP forum filtering judgments are influenced when expectations formed on the basis of contextual cues are confirmed or disconfirmed by the examination of solution quality. We summarize six different models of expectation confirmation explored in previous IS literature and report the results of a neuroimaging experiment using functional MRI (fMRI) that paired both positive and negative contextual cues with high- and low-quality solutions on a mock ENP forum interface. Results show that evaluation judgments are strongest in conditions where initial contextual cue judgments are confirmed by examination of solution quality except when the perceived expectation-experience gap is large, providing evidence for an assimilation-contrast model of expectation confirmation. We also found neural activation differences for expectation confirmation vs. disconfirmation and, consistent with prospect theory, differences in filtering behaviors with respect to unexpected gains vs. unexpected losses

Chasing probabilities — Signaling negative and positive prediction errors across domains

AbstractAdaptive actions build on internal probabilistic models of possible outcomes that are tuned according to the errors of their predictions when experiencing an actual outcome. Prediction errors (PEs) inform choice behavior across a diversity of outcome domains and dimensions, yet neuroimaging studies have so far only investigated such signals in singular experimental contexts. It is thus unclear whether the neuroanatomical distribution of PE encoding reported previously pertains to computational features that are invariant with respect to outcome valence, sensory domain, or some combination of the two. We acquired functional MRI data while volunteers performed four probabilistic reversal learning tasks which differed in terms of outcome valence (reward-seeking versus punishment-avoidance) and domain (abstract symbols versus facial expressions) of outcomes. We found that ventral striatum and frontopolar cortex coded increasingly positive PEs, whereas dorsal anterior cingulate cortex (dACC) traced increasingly negative PEs, irrespectively of the outcome dimension. Individual reversal behavior was unaffected by context manipulations and was predicted by activity in dACC and right inferior frontal gyrus (IFG). The stronger the response to negative PEs in these areas, the lower was the tendency to reverse choice behavior in response to negative events, suggesting that these regions enforce a rule-based strategy across outcome dimensions. Outcome valence influenced PE-related activity in left amygdala, IFG, and dorsomedial prefrontal cortex, where activity selectively scaled with increasingly positive PEs in the reward-seeking but not punishment-avoidance context, irrespective of sensory domain. Left amygdala displayed an additional influence of sensory domain. In the context of avoiding punishment, amygdala activity increased with increasingly negative PEs, but only for facial stimuli, indicating an integration of outcome valence and sensory domain during probabilistic choices

Processing of unpredictability in fear learning and memory

Unpredictability is one of the major drivers of associative learning. While unpredictability in the timing of events can enhance fear memory strength, the neural substrates that are involved in generating and processing these errors remain largely unknown. We first showed that unpredictability, generated by the varied timing of the aversive event following the predictive cue, greatly enhanced fear memory strength (Chapter 3). The unpredictability-processing neural network in basal and lateral amygdala (BLA) was then studied using time-lapse microendoscopy to monitor neuronal calcium response across fear conditioning and recall (Chapter 4). We identified four distinct functional classes of neurons based on the neuronal activity patterns during fear conditioning and long-term recall. “Memory Winner” neurons outcompeted the “Memory Loser” neurons to encode the fear memories; nonetheless, both classes of neurons exhibited learning-related plasticity during the fear conditioning. In contrast, Fear Expression neurons did not display learning-related plasticity during fear conditioning but did respond to the tone presentation during auditory fear recall. The introduction of temporal unpredictability during the fear conditioning increased the percentage of both the Memory Winner neurons and Fear Expression neurons, and decreased the percentage of Memory Loser neurons. Furthermore (Chapter 5), pharmacological inhibition of dorsal hippocampus and optogenetic silencing of CA1 revealed the essential involvement of dorsal hippocampus in the processing of negative prediction errors, which is generated by unpredictability in their timing. Collectively, our data suggest that the processing of temporal unpredictability of aversive events requires the dorsal hippocampal activation to process the negative prediction errors; and the rearrangement of the BLA neural representation of fear learning and memory. Taken together, these processes underlie the mechanism of the unpredictability-enhanced fear memory strength

Cortical and subcortical contributions to human cognitive flexibility

Cognitive flexibility enables individuals to respond adaptively to an ever-changing world. Neurally, flexibility is underpinned by involvement from across the cerebrum, and there is evidence from animal and human neuroscience suggesting that integration of cortical and thalamic signals in the striatum is necessary for appropriate behavioural control. A commonly used assay of flexibility is reversal learning, an associative learning task with high inter-species translatability. Evidence from animal literature has clearly defined the importance of the striatal cholinergic system in regulating striatal activity and output from the basal ganglia, and there is nascent evidence suggesting this system operates in a similar way in humans. However, there is a need to further disentangle the role of cortical, striatal, and thalamic regions during reversal learning in humans to better understand how the system works, and whether it has heterogeneous functionality in different contexts. Furthermore, as studying these processes is not trivial, further methodological work is required to enable us to understand the system. In chapter two we systematically assess an automated parcellation technique for identifying specific thalamic nuclei. Despite generally being treated as a homologous structure in neuroimaging work, nuclei within the thalamus have dissociable roles, and have diverse contributions to cognitive functioning, including reversal learning. We found mixed efficacy for segmentations across the thalamus, with some regions being more accurately defined relative to a “gold standard” atlas than others. Crucially, we find that the centromedian and parafascicular nuclei, which have an important role in reversal learning, are clearly defined and have little overlap with contiguous regions. These results show we can use this automated parcellation technique to identify specific thalamic nuclei that are relevant for cognitive flexibility and use these parcellations to study functionally relevant processes. Recent work has demonstrated that the functional relevance of the striatal cholinergic system can be studied in vivo using magnetic resonance spectroscopy by separating the peaks of different metabolites. But this non-conventional approach has not yet been widely adopted, and work is needed to determine its reliability. Chapter three presents test-retest reliability data on the use of magnetic resonance spectroscopy to study cholinergic activity in the striatum and cortex. We find measures of choline containing compounds are highly correlated when peaks are separated and when they are not. Across time we find that choline concentrations are relatively inconsistent, and that this was due to changes in the functionally relevant metabolite choline. Conversely, metabolites that we think are not functionally relevant were stable over time. We believe these differences may underly differences in acetylcholine function over time and may explain some intra-individual behavioural variability. In chapter four we use functional magnetic resonance imaging and psychophysiological interaction analysis to study corticostriatal and thalamostriatal connectivity during serial reversal learning. Functional connectivity between the centromedian-parafascicular nuclei of the thalamus and the associative dorsal striatum, and between the lateral-orbitofrontal cortex and the associative dorsal striatum was related to processing feedback during reversal learning. Specifically, thalamostriatal connectivity was found across the task, and may reflect a general error signal used to identify potential changes in context. Conversely, corticostriatal connectivity was found to be specific to when behaviour changed and suggests this may be a mechanism for the implementing adaptive change. We also show findings from exploratory work that may explain further how the cortex supports flexibility during reversal learning. Lastly, we used magnetic resonance spectroscopy to investigate whether the state of the cholinergic system at rest is related to reversal learning performance and latent measures of behaviour using computational modelling. Choline concentrations at rest showed significant functional relevance to our measures of reversal learning. More specifically, we found that errors during reversal learning, and learning rates for positive and negative prediction errors, explained significant variance in choline. However, the relationship between choline levels and task performance presented here differ from previous work which instead used a multi-alternative reversal learning task, and suggests that the striatal cholinergic system may have dissociable roles in different contexts. Overall, we show that the striatum, its cholinergic interneuron system, and its afferent projections from the cortex and thalamus, are associated with performance during serial reversal learning. Moreover, these findings suggest that the system may operate in separable ways in different contexts which may be dependent on internal representations of task structure