Associate Professor, Neurology University of California, San Francisco, United States
Introduction: Many current therapies fail to provide enduring relapse prevention to patients with substance use disorders (SUDs), with an estimated 40-60% of treated patients relapsing after voluntary abstinence within 6 months. Deep brain stimulation (DBS) is currently being used to address neurological and psychiatric diseases lacking practical treatment options. By recording local field potentials (LFPs) in the limbic pallidum (LP) of SUD patients, we hope to unveil biomarkers of neural oscillations related to drug cravings. Previous research implicates the LP as a therapeutic target for this study since it plays an important role in drug-seeking behaviors. With an understanding of the biomarkers, DBS could provide a long-lasting treatment option for SUD patients.
Clinically, DBS is effective and often used for treating Parkinson’s Disease, dystonia and essential tremor. DBS also shows promise for neuropsychiatric disorders such as OCD and treatment-resistant depression. New efforts have been focused on developing DBS and machine learning algorithms to detect and respond to brain activity changes to improve DBS efficacy. Our research aims to eventually detect and suppress neural activity in the LP related to drug cravings.
Materials and
Methods: The subject of this study, an adult male diagnosed with severe alcohol use disorder and currently attempting abstinence, underwent bilateral DBS implantation surgery targeting the LP. The implanted electrodes, each equipped with four contacts, could both record LFPs and stimulate.
Postoperatively, LFPs were recorded on 20 days over the course of a year. Recordings either occurred in a hospital setting, during monthly scheduled visits, or when the subject reported cravings from his home. The LFPs were analyzed using frequency-domain techniques, such as power spectral density analysis, to identify oscillatory patterns. The analysis focused on observing correlations between clinical data, such as alcohol use following or preceding a recording, and increases in neural activity oscillations within specific frequency bands. The following analyses split the recordings between heavy or light drinking within a week following the recording. In accordance with the NIAAA, heavy drinking is defined as more than 5 drinks in a day or more than 15 drinks in a week for an adult male.
Results, Conclusions, and Discussions: As seen in Figure 1, there is an observable difference in alpha wave power in the LP preceding a week of no/light drinking compared to a week of heavy drinking. Relative power is used to quantify this activity and is calculated by integrating the power spectral density across a specific range of frequencies. Here, the range of interest is 9 to 14 Hz since that is where the peak is located. A significant difference between the relative powers calculated across this range for each condition is observed in Figure 2. Additionally, Figure 3 displays the change in relative power over time as well as the total number of drinks within a week following the recording. Relevant external factors that could change either of these measurements have also been labeled.
The results show an increase in alpha wave activity in the LP as a potential biomarker of alcohol use. As observed in Figure 1, the difference in the periodic power is, on average, about 10 times greater when the recording precedes a week of heavy drinking. A noticeable correlation between relative power and total number of drinks is also displayed in Figure 3 prior to stressful life events occurring.
In the future, testing the acute effects of suppressing increased alpha wave activity in the LP using targeted DBS is essential to assess changes in craving and drinking behavior. Replication across a diverse patient population is also necessary to confirm the reliability and generalizability of the findings. Furthermore, integrating machine learning algorithms could enhance DBS precision by altering stimulation in real-time, offering an interdisciplinary approach that aims to develop effective, long-lasting treatments for substance use disorders.
