A series of three neuroimaging studies identified a pattern of neural activation involving specific brain regions that differentiates drug users from non-users with 82% accuracy. Researchers named the pattern the Neurobiological Craving Signature (NCS). Their findings have been published in Nature Neuroscience.
Craving is a strong desire to use drugs or eat. It has long been considered a key factor driving substance abuse and overeating. It is one of the criteria used for diagnosing substance use disorders. Craving is often induced by exposure to certain stimuli. In the case of overeating, these include the smell or sight of food. In the case of drugs, craving can be induced by one being in places or situations he/she associates with taking drugs or being offered drugs. This is called cue-induced craving.
Earlier studies of craving have successfully relied on self-reported craving, but recent research has focused on discovering its biological basis. Human neuroimaging studies have identified neural circuits related to the risk of substance abuse. Some brain circuits have been found to be involved in different substance use disorders and risky behaviors. These include specific parts of the ventromedial prefrontal cortex (vmPFC), ventral striatal/nucleus accumbens (VS/NAc) and insula regions of the brain. These regions also appear to play a role in weight gain and obesity.
However, just identifying areas of the brain that show distinct activity in certain situations is not enough to infer subjective feelings (such as craving) from brain activity. Studies have shown that neural activity related to specific mental states are distributed across different regions of the brain. That is why attempts are made to create mathematical models that integrate activities of diverse brain regions to allow inferences about mental states and health outcomes with larger degrees of accuracy. Such models are called neuromarkers.
Study author Leonie Koban and her colleagues wanted to develop a neuromarker that could predict the intensity of drug and food craving. They integrated data from five different groups into three studies using functional magnetic resonance imaging on users of cigarettes, alcohol and cocaine. They compared this with images of brains of persons who did not use any of these substances nor any other drug. A total of 99 participants were involved in the studies.
“Across studies, participants were presented with visual cues of drugs and highly palatable food items. We then used machine learning to identify a distributed functional brain activity pattern that predicted the intensity of craving. We term the resulting pattern the Neurobiological Craving Signature (NCS), and we hope that this name reduces ambiguity and provides a reference point for the pattern’s future reuse and testing in new samples,” the study authors explained.
In the first study, researchers showed pictures of cigarettes and food to cigarette smokers (21 persons) and non-smokers (22). In the second study, they showed pictures of alcoholic drinks and food to alcohol users (17 persons). In study three, they showed pictures of cocaine and food to cocaine users (21 person) and non-users (18 participants).
Results showed that the Neurobiological Craving Signature (NCS) model predicted craving with success in all groups, but that it was the most effective in predicting craving in non-smokers (study 1) and non-users of cocaine (study 3). Although still quite effective, the model was the least effective in predicting craving in cigarette smokers. When researchers explored the ability of their model to differentiate between high and low levels of craving, it was found to be accurate in between 72% and 81% of cases.
The Neurobiological Craving Signature mode was only 60% accurate in differentiating cocaine users from non-users when they were being shown pictures of food. However, this rose to 75% when they were being shown pictures of cocaine. When the differences in neural activity when shown pictures of food and of cocaine was considered, the accuracy of differentiation between cocaine users and non-users rose to 82%. Results also showed that brain activity patterns in drug and food craving are similar.
The Neurobiological Craving Signature (NCS) model was based on neural activity in ventromedial prefrontal and cingulate cortices, ventral striatum, temporal/parietal association areas, mediodorsal thalamus and cerebellum regions of the brain.
“In both Western and Eastern philosophy, craving has been considered a source of suffering and unhappiness. Although craving is an important feature of substance use disorders, eating disorders and other psychiatric conditions, it is also a general aspect of human experience,” the study authors concluded.
“In this study, we used machine learning to identify a distributed brain pattern—that we term the NCS as a reference for future use—that tracks the degree of craving when applied to new individuals, across different diagnostic groups, scanners and scanning parameters. Notably, this pattern separated drug users from non-users based on brain responses to drug cues but not food cues. Thus, it is an important step toward a diagnostic neuromarker of substance use.”
The study makes an important contribution to scientific knowledge of neural basis of craving. However, it also has limitations that need to be taken into account. Notably, the study used “a limited set of highly appetitive cues” [pictures]. The study also did not use non-craving control conditions in the same groups of participants.
The study, “A neuromarker for drug and food craving distinguishes drug users from non-users”, was authored by Leonie Koban, Tor D. Wager, and Hedy Kober.
