Matthis, C. | Search bioRxiv

bioRxiv2019

Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls

While an increased impact of cues on decision-making has been associated with substance dependence, it is yet unclear whether this is also a phenotype of non-substance related addictive disorders, such as gambling disorder. To better understand the basic mechanisms of impaired decision-making in addiction, we investigated whether cue-induced changes in decision-making could distinguish gambling disorder (GD) from healthy control (HC) subjects. We expected that cue-induced changes in gamble acceptance and specifically in loss aversion would distinguish GD from HC subjects. 30 GD subjects and 30 matched HC subjects completed a mixed gambles task where gambling and other emotional cues were shown in the background. We used machine learning and classification to carve out the importance of cue-dependency of decision-making and of loss aversion for distinguishing GD from HC subjects. Cross-validated classification yielded an area under the receiver operating curve (AUC-ROC) of 68.9% (p=0.002). Applying the classifier to an independent sample yielded an AUC-ROC of 65.0% (p=0.047). As expected, the classifier used cue-induced changes in gamble acceptance to distinguish GD from HC. Especially increased gambling during the presentation of gambling cues was characteristic of GD subjects. However, unexpectedly, cue-induced changes in loss aversion were irrelevant for distinguishing GD from HC subjects. To our knowledge, this is the first study to investigate the classificatory power of addiction-relevant behavioral task parameters when distinguishing GD from HC subjects. The results indicate that cue-induced changes in decision-making are a characteristic feature of addictive disorders, independent of a substance of abuse.

neuroscience↗

bioRxiv2018

Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

Background: Just as substance use disorders (SUDs), gambling disorder (GD) is characterized by an increase in cue-dependent decision-making (similar to Pavlovian-to-instrumental transfer, PIT). PIT, as studied in SUDs and healthy subjects, is associated with altered communication between Nucleus Accumbens (NAcc), amygdala, and orbitofrontal cortex (OFC). These neural differences are, however, poorly understood. For example, it is unclear whether they are due to the physiological effects of substance abuse, or rather related to learning processes and/or other etiological factors like innate traits associated with addiction. We have thus investigated whether network activation patterns during a PIT task are also altered in GD, an addictive disorder not involving substance abuse. We have specifically studied which neural PIT patterns were best at distinguishing GD from HC subjects, all to improve our understanding of the neural signatures of GD and of addiction-related PIT in general. Methods: 30 GD and 30 HC subjects completed an affective decision-making task in a functional magnetic resonance imaging (fMRI) scanner. Gambling associated and other emotional cues were shown in the background during the task, allowing us to record multivariate neural PIT signatures focusing on a network of NAcc, amygdala and OFC. We built and tested a classifier based on these multivariate neural PIT signatures using cross-validated elastic net regression. Results and Discussion: As expected, GD subjects showed stronger PIT than HC subjects because they showed stronger increase in gamble acceptance when gambling cues were presented in the background. Classification based on neural PIT signatures yielded a significant AUC-ROC (0.70, p = 0.013). When inspecting the features of the classifier, we observed that GD showed stronger PIT-related functional connectivity between NAcc and amygdala elicited by gambling background cues, as well as between amygdala and OFC elicited by negative and positive cues. Conclusion: We propose that HC and GD subjects are distinguishable by PIT-related neural signatures including amygdala-NAcc-OFC functional connectivity. Our findings suggest that neural PIT alterations in addictive disorders might not depend on the physiological effect of a substance of abuse, but on related learning processes or even innate neural traits, also found in behavioral addictions.

neuroscience↗