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

Abstract

Background:Just as substance use disorders (SUD), gambling disorder (GD) is characterized by an increase in cue-dependent decision-making (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). However, these neural differences are 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 healthy control (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 Alexander Genauck 5 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 nucleus accumbens (NAcc) and amygdala elicited by gambling background cues, as well as between amygdala and orbitofrontal cortex (OFC) elicited by negative and positive cues. Conclusion:We propose that GD and HC 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.Alexander Genauck 6

Functional connectivity in reward‐related networks is associated with individual differences in gambling strategies in male Lister hooded rats

“…The risky gambling strategy was correlated with higher CDI in the nucleus accumbens, caudate putamen and amygdala and higher CC in caudate putamen and nucleus accumbens. This agrees with findings from studies of patients with GD that reported higher functional connectivity between nucleus accumbens and amygdala 48 as well as increased R-fMRI connectivity in the network including cingulate cortex, caudate nucleus and nucleus accumbens compared with healthy controls. 49 Moreover, a study using a cued version of the rGT showed that inhibition of dopaminergic projections to the nucleus accumbens induced a switch from risky to optimal gambling strategies in male rats.…”

“…It cannot be completely ruled out that the R‐fMRI pattern characterizing the risky rats is caused by performing the rGT. However, the detected areas herein cohere with regions found to be significant for human volunteers engaging in the IGT 30 as well as for GD patients 20,48,49 . Additionally, the brain functional connectivity networks are largely stable and suggested to be innate 32 .…”

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

“…on neural level. 34 Moreover, the above machine learning studies did not use an independent validation sample to corroborate their results. Our independent validation yielded an AUC-ROC of 0.65.…”

“…For final validation of the fitted classifier we used a sample from another study where subjects performed the affective mixed gambles task in a functional magnetic resonance imaging (fMRI) scanner (see Table S2). 34 2.2 | Procedure and data acquisition…”

Altered orbitofrontal sulcogyral patterns in gambling disorder: a multicenter study

“…A total of 177 pathological gamblers (PGs) and 169 healthy controls (HCs) were included in the present study. These data were pooled from nine separate previous neuroimaging studies 8,15,18,[43][44][45][46][47][48] (Supplementary Table 1). From this aggregated sample, 18 participants (10 PGs and 8 HCs) were excluded because of head movement artefacts on anatomical T1 scans preventing us from reliably identifying OFC sulcogyral patterns, and 4 participants (2 PGs and 2 HCs) were excluded because either demographic or diagnostic information was missing.…”