In a world first, a study tests brain implants to stop binge eating

In a ground-breaking pilot study, researchers surgically implanted a device into the brains of two obese binge-eating problem sufferers.

With encouraging results, establishing the groundwork for a future in which implants can manage a range of impulsive behaviors, the gadget was created to identify and block brain signals linked to binge-eating food cravings.

The new study was carried out by Perelman School of Medicine at the University of Pennsylvania (UPenn) and published on Monday, August 29 in Nature Medicine.

According to the researchers from UPenn, a small device that detects food craving-related brain activity in a key brain region and that responds by electrically stimulating that region showed promise in a pilot clinical trial. The trial showed promise in two patients with loss-of-control binge eating disorder (BED).

Notable decreases in binge-eating

The patients were closely monitored over a period of six months, and, according to the researchers, the devices appeared to be operating as intended with no apparent side effects. Both patients noted notable decreases in the frequency of binge-eating episodes, as well as diminished sensations of being out of control. Over the next six months, each patient shed an additional 11 lb (5 kg) on average–without receiving any specific dietary advice.

“This was an early feasibility study in which we were primarily assessing safety, but certainly the robust clinical benefits these patients reported to us are really impressive and exciting,” said senior author of the study Casey Halpern," according to New Atlas.

The primary objectives of this initial pilot study were safety and feasibility, Halpern emphasized. It is therefore too soon to say whether or not this type of brain stimulation technique genuinely works to control binge-eating, but these preliminary results do demonstrate the device's safety.

Binge-eating is very common in the U.S.

As the UPenn team suggests, binge-eating disorder is considered the most common eating disorder in the United States, affecting at least a few million people.

It frequently involves binge-eating episodes without bulimia-like purging and is frequently linked to obesity. The person who is bingeing feels as though they are losing control over their food, so they eat past when they normally feel full.

Cravings for certain preferred meals occur by binge-eating disorder episodes. In a 2018 study using mice and humans, Halpern and his colleagues discovered evidence that specific low-frequency electrical activity in the nucleus accumbens emerges shortly before these cravings—but not before regular, non-binge-eating.

In order to stop this craving-related activity whenever it happened, the researchers activated the nucleus accumbens in mice. They discovered the mice consumed substantially less of a delectable, high-calorie diet than they otherwise would have.

There is still a lot of work to be done

Even if this research does point toward a future in which brain implants might be able to control impulsive behaviors like excessive eating, there is still a lot of work to be done before researchers fully understand how to achieve this. Additional participants will be sought for this ongoing study to further hone the technology.

Study abstract

Cravings that precede loss of control (LOC) over food consumption present an opportunity for intervention in patients with binge eating disorder (BED). In this pilot study, we used responsive deep brain stimulation (DBS) to record nucleus accumbens (NAc) electrophysiology during food cravings preceding LOC eating in two patients with BED and severe obesity (trial registration no. NCT03868670). Increased NAc low-frequency oscillations, prominent during food cravings, were used to guide DBS delivery. Over 6 months, we observed improved self-control of food intake and weight loss. These findings provide early support for restoring inhibitory control with electrophysiologically-guided NAc DBS. Further work with increased sample sizes is required to determine the scalability of this approach.