Summary: Researchers have identified neural biomarkers associated with food and drug cravings. The findings could help pave the way for new treatments for addiction.
Craving is known to be a key factor in substance abuse disorders and increases the likelihood of future drug use or relapse. But the neural basis — or how the brain learns — is not well understood.
In a new study, researchers from Yale, Dartmouth and France’s National Center for Scientific Research (CNRS) have identified stable brain patterns, or neuromarkers, for drug and food cravings. Their findings were published in Nature Neuroscience.
The discovery could be an important step toward understanding mental need, addiction as a brain disorder, and how to better treat addiction in the future, researchers said. Most importantly, this neuromarker can be used to distinguish drug users from non-users, which could one day be used to diagnose not only neural need, but also drug use disorders.
For many diseases, there are biological markers that doctors can use to diagnose and treat patients. To diagnose diabetes, for example, doctors test a blood marker called A1C.
“One of the benefits of having a stable biological marker for a disease is that you can give the test to anyone and tell them whether or not they have the disease,” said Heidi Kober, associate professor of psychiatry at Yale School of Medicine. and the author of the study. “And we don’t have it for psychopathology and certainly not for addiction.”
To determine whether such a signal could be established for interest, Kober and her colleagues—Leonie Coban of CRNS and Thor Wager of Dartmouth College—used a machine learning algorithm. The idea is that if many individuals who experience similar levels of desire share brain activity, a machine learning algorithm can recognize that pattern and use it to predict levels of desire based on brain images.
For the study, they used functional magnetic resonance imaging (fMRI) data from 99 human subjects to provide insights into brain activity to train and test a machine learning algorithm.
The fMRI data was collected while the subjects—self-identified as drug users or non-users—viewed pictures of drugs and highly palatable foods. Participants rated how strongly they desired the items they saw.
An algorithm identified patterns of brain activity that could be used to predict drug and food cravings from fMRI images alone, the researchers said.
The pattern they observed – which they called the “neurobiological craving signature (NCS)” – involved activity in several areas of the brain, some of which previous studies had linked to drug use and craving.
However, the NCS also provides a new level of detail in how neural activity in these brain regions predicts desire.
“It gives us a better understanding of how these regions interact and predict the personal experience of desire,” Kober said.
NCS also revealed that brain responses to drug and food cues are similar, suggesting that drug craving comes from the same neural systems that generate food cravings. Importantly, the index was able to distinguish drug users from non-users based on their brain responses to drug cues, but not to food cues.
“And these findings are not specific to any one substance because we included participants who used cocaine, alcohol, and cigarettes, and the NCS predicts craving across all of them,” Kober said. “So, it’s really a biomarker of craving and addiction. In all of these substance use disorders, there are common elements that are caught during cravings.
Wager suggests that seemingly similar emotional and motivational processes actually involve different brain pathways and can be measured in different ways.
“What we see here is not a general signature for ‘rewards’, but a more selective one for food and drug needs,” he said.
Additionally, NCS also provides a new cognitive target for better understanding how food and drug cravings are influenced by contextual or emotional factors. “For example,” Coban said, “we could use the NCS in future studies to measure how stress or negative emotions increase the desire to use drugs or eat our favorite chocolate.”
Note that while Kober NCS is promising, it needs further validation and is not yet ready for clinical use. This may be a few years down the road. Now, she—along with her team and collaborators—are working to better understand this network of brain regions and see if NCS can predict how people with substance use disorders will respond to treatment.
That, she said, makes this neuromarker a powerful tool to inform treatment strategies.
“Our hope is that the brain, and specifically the NCS, as a stable biological marker, will allow us to not only identify substance use disorders, but also to understand differences in outcomes among people,” Kober said. It responds to certain treatments.
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“Neuromarkers of drug and food craving distinguish drug users from non-usersBy Heidi Kober et al. Nature Neuroscience
Neuromarkers of drug and food craving distinguish drug users from non-users
Craving is a core feature of substance use disorders. It predicts substance use and relapse and is associated with binge eating, gambling, and other maladaptive behaviors.
Interest is measured by self-report, which is limited by internal accessibility and sociocultural contexts. Neurobiological markers of craving are both needed and lacking, and it is not yet clear whether drug and food craving involve similar mechanisms.
From three functional magnetic resonance imaging studies (n= 99), we used machine learning to identify cross-validated neuromarkers that predicted self-reported drug and food craving intensity.P<0.0002).
This pattern, which we call the neurobiological craving signature (NCS), involves ventromedial prefrontal and cingulate cortices, ventral striatum, temporal/parietal association areas, mediodorsal thalamus, and cerebellum.
Importantly, NCS responses to drug and food cues discriminated between drug users and nonusers with 82% accuracy. NCS is also adapted to the self-directed strategy. Crosstalk between distinct neural markers for drug and food craving suggests common neurobiological mechanisms.
Future studies could assess the discriminant and convergent validity of the NCS and test whether it predicts response to clinical interventions and long-term clinical outcomes.